# EvoX Intelligent Evolution System and AI Native Research Practice — Course Lecture Notes

Class Hours: 1 · Courseware Pages: 16

## Reading Navigation

- [Lesson 5 | Topic: Method Evolution: From Optimizing Solutions to Optimizing the Algorithm Itself](#lecture-lesson-1)

---

<a id="lecture-lesson-1"></a>

## Lesson 5 | Topic: Method Evolution: From Optimizing Solutions to Optimizing the Algorithm Itself

<a id="lecture-page-1"></a>

### Method Evolution: From Optimizing Solutions to Optimizing the Algorithm Itself

*Page 1*

> **Page Objective:** Explore method evolution mechanisms such as MetaDE and AutoPSO, and learn how to incorporate the methods that produce candidates into the optimization object, achieving self-evolution of algorithms.

> **Page Objective**
>
> Understand the core idea of method evolution (Meta-Evolution), master the architecture of delegating mutation strategies and hyperparameters to outer-layer evolutionary optimization, and achieve the leap from optimizing solutions to optimizing the algorithm itself.

> **Lecture Sequence**
>
> Given a problem, optimize candidate solutions.
>
> The mutation strategies and hyperparameters of algorithms (such as Differential Evolution DE, Particle Swarm Optimization PSO) typically rely on manual design or empirical tuning.
>
> Treat algorithm configurations (strategies, parameters) as individuals in the outer-layer evolution, with the inner-layer algorithm responsible for solving the original problem.
>
> The outer-layer evolution automatically searches for the best mutation strategy and hyperparameter combinations.
>
> 5. Summarize the dual-layer optimization architecture and the significance of self-evolution.

**Key Teaching Points**

- The optimization object moves up from "solutions" to "methods/algorithms." Dual-layer optimization. The outer-layer evolutionary algorithm optimizes the configuration of the inner-layer optimizer (such as mutation strategy, crossover rate, inertia weight, learning factors, etc.)
- The outer-layer fitness is the inner layer's performance on validation problems (such as average optimal value, convergence speed). Automatically select or generate mutation strategies and tune parameters for DE.
- Automatically configure PSO parameters and topology structure. Reduce manual tuning, discover novel strategies, and improve cross-problem generalization capability.
- Nested optimization leads to significantly increased computational overhead.

**Comprehension Check**

Ask students to answer — In method evolution, what do individuals in the outer-layer algorithm represent? What role does the inner-layer algorithm play? How is fitness defined?

> **Transition Hint**
>
> After understanding the dual-layer architecture, subsequent content can elaborate on the implementation details and experimental results of MetaDE or AutoPSO.

<a id="lecture-page-2"></a>

### Core Background and Motivation for Method Evolution

*Page 2*

> **Page Objective:** Learners should understand the limitations of manual presetting that traditional optimization methods face after computational power expands.

**Key Teaching Points**

- The one-sentence version of method evolution is: <em>solutions are evolving, and the methods that produce solutions are also evolving</em>. When you see any approach that "only tunes parameters without changing mechanisms," you can use this to judge whether you are stuck at the first level or have risen to the second level.
- Hyperparameter tuning is typically an offline search performed once before running, selecting a value and then fixing it — essentially still manual presetting. Method evolution treats the method itself as a search object during the running process, continuously adjusting with feedback. The criterion is: does this mechanism for producing candidates change during operation?
- Can you articulate the difference between the left and right sides? If you can only say "the right side computes faster and finds better results," you haven't grasped the key point; the key point is that the right side has one more layer of search objects — the method itself enters the cycle of being searched, evaluated, and updated.

[Page Objective]

When computational scale no longer constitutes a bottleneck, what truly constrains evolutionary effectiveness is that "the method for producing candidate solutions itself is still manually preset and fixed for the long term." Only by understanding this shortcoming can we then discuss incorporating mechanisms such as mutation strategies, scaling factors, and crossover rates into the evolutionary object.

[Lecture Sequence]

It is recommended to proceed along the line of "computational power unlocked → bottleneck shifts → boundaries of traditional optimization → problem level moves up → analysis and groundwork," finally landing on classroom exercises and case discussions.

[Key Teaching Points]

1. Why the bottleneck shifts. On EvoX's modern computational foundation, populations, evaluation results, and algorithm states can enter acceleration devices or distributed clusters as a whole, and tens of thousands of candidate evaluations no longer need to be processed serially one by one. After the computational power layer is unlocked, the remaining limitation falls on the "search method": the method for producing candidate solutions is still manually preset and remains unchanged during operation.

2. The boundaries of traditional optimization. Traditional optimization algorithms are very good at finding solutions within a given search space — give it a mutation strategy, a set of parameters, a neighborhood structure, and it will repeatedly generate candidate solutions, evaluate, and select. But note that this mechanism for producing candidates is an externally designed constant, and the algorithm will not change it throughout the entire running process. In other words, only "solutions" are being searched; "the way solutions are produced" is not being searched. This brings two consequences: the system can only repeat established routines, making it difficult to adaptively expand with problems, environments, and feedback; although the algorithm can search for solutions, it itself does not participate in improving the search process.

The problem moves up one level. Method evolution turns "how to produce candidate solutions" from an external design choice into an object that can be searched and evolved. Taking Differential Evolution as an example, the mutation strategy, scaling factor F, and crossover rate CR — settings that were originally manually fixed — can all enter the evolutionary loop and be continuously adjusted with feedback. Thus the system searches at two levels simultaneously: "solutions" and "the way solutions are produced." When problem forms change, environments drift, or feedback signals change, the method level can also be rewritten accordingly, rather than letting a fixed heuristic strategy handle all scenarios rigidly.

4. Analysis hint. Method evolution is not equal to "switching to a better algorithm." Switching algorithms is still a one-time manual choice, fixed once selected; method evolution emphasizes that this mechanism for producing candidates is itself in a state of being searched and continuously adjusted. The criterion can be condensed into one sentence: does this mechanism for producing candidates change during operation?

5. Why this motivation is the foundation for subsequent content. It draws the watershed between EvoX and ordinary optimizers — how the boundaries of evolutionary objects continue to expand outward, how the algorithm itself is improved, and how the system moves toward cascading and continuous evolution — all built on this layer of "moving up." It can be said that the computational foundation solves "whether we can compute at scale," while method evolution solves "whether the search method can improve alongside large-scale computation."

[Memory Anchor]

Solutions are evolving, and the methods that produce solutions are also evolving. When you see any approach that "only tunes parameters without changing mechanisms," you can use this to judge whether you are stuck at the first level or have risen to the second level.

[Exercise Handling]

This page has five multiple-choice questions, which can be handled together after the lecture, pointing out the basis for judgment for each question:

Regarding the fundamental limitation of traditional optimization, the correct option falls on "only finding solutions within a given search space, with the mechanism for producing candidates manually preset and not improved." If students choose "computes slowly" or "cannot parallelize," remind them that is a problem at the computational foundation level, while this page discusses whether the search method can be improved.

Regarding why the bottleneck becomes prominent after computational power is unlocked, the key is "a fixed method can only repeat established routines, making it difficult to adaptively expand with problems, environments, and feedback." Tensorization and Runtime are precisely for efficient batch computation, so the bottleneck is not "whether we can compute" but "whether the search method can change."

Regarding "the problem moves up one level," emphasize that the search object expands from "solutions" to "the method for producing solutions," rather than changing hardware or rewriting the problem form.

Regarding what is evolved in Differential Evolution, the answer is mutation strategy, scaling factor F, and crossover rate CR; the optimal value is the result being searched, while hardware and data are external conditions.

Regarding the foundation this page's motivation provides for subsequent content, it falls on "expansion of evolutionary object boundaries, improvement of the algorithm itself, and system-level continuous evolution."

[Case Discussion]

The team fixed the mutation strategy, F=0.5, CR=0.9 after offline trial runs, and then made no changes for a long time; half a year later, the city added restricted traffic zones, and the order structure changed from "small batches, many locations" to "large batches, few locations." Solution quality continued to decline, and every parameter adjustment required redoing offline trial runs, with response cycles measured in weeks.

The problem is that they only search at the "candidate routes" level; the mechanism for "how to produce candidate routes" is manually preset and fixed for the long term, which is precisely the boundary of traditional optimization. If method evolution were introduced, what enters the search space would be mutation strategy selection, F, CR, and related parameter combinations and strategy scheduling rules, all of which change from constants to objects that can be continuously adjusted through evaluation result feedback.

The team can already complete millions of candidate evaluations per day on acceleration devices, indicating that the "can't compute" bottleneck has basically been unlocked, and the remaining shortcoming is precisely that "the search method is fixed." After incorporating the method level into evolution, the additional method search overhead can share the same batch parallel capability with solution search, keeping marginal costs controllable. A reasonable implementation approach is to treat mutation strategies and F, CR as evolvable states attached to the population tensor, entering the batch computation graph together with other states; evaluation results serve as feedback signals returned to the method layer, updating strategy and parameter distributions at a certain pace; meanwhile, retain offline baseline configurations for comparison, evaluating net gains using solution quality under the same computational budget and response deployment time.

[Comprehension Check]

Ask students to articulate the difference between traditional single-layer optimization and method evolution. If they can only say "the right side computes faster and finds better results," they haven't grasped the key point; the key point is that the right side has one more layer of search objects — the method itself enters the cycle of being searched, evaluated, and updated.

[Transition Hint]

Next, this layer of "moving up" will be grounded into specific mechanisms, examining how the algorithm itself is improved.

<a id="lecture-page-3"></a>

### Technical Evolution and Historical Origins of Intelligent Systems

*Page 3*

> **Page Objective:** Learners should grasp the position of evolutionary computation as a technical source in the convergence of multiple routes.

> **Page Objective**
>
> Pull the technical history of intelligent systems back from the misreading of "single-line evolution," enabling learners to grasp the position of evolutionary computation in the convergence of four source streams, and thereby understand EvoX's architectural orientation — treating "continuous evolution" as a runnable system capability, not an algorithmic trick.

**Key Teaching Points**

- 1. Four source streams, four ways of asking questions. Reading technical history as a straight line will severely misjudge today's intelligent systems. Symbolism cares about "rules and symbols," characterizing intelligence through explicit knowledge representation and logical reasoning, placing "explaining clearly" before "computing results," at the cost of difficulty in handling fuzzy, high-dimensional, noisy reality.
- Connectionism cares about "data and representation," using neural networks and distributed representation for parameter learning, skilled at fitting complex mappings, but typically requiring an explicitly specified objective and labels.
- Cybernetics cares about "feedback and closed loops," understanding intelligence as a continuous regulation process between system and environment; it provides the mechanism of "continuous correction" but leaves "what to explore" to search strategies.
- Evolutionary computation cares about "population, mutation, selection," carrying diversity through populations, generating change through mutation and recombination, and preserving effective structures through evaluation and selection. It does not answer "what is the correct answer" but answers "how to continuously improve in an open space." When encountering any technical history narrative, first use these four keywords to locate which source stream it belongs to, and judgment will be much faster. 2. Convergence is not replacement. The three classical routes provide capabilities of representation, generation, and fitting; evolutionary computation provides mechanisms of open search, adaptation, and continuous improvement.
- The two combined precisely cover two different levels of problems: "knowing how to compute" and "knowing where to go." Therefore any claim that "a certain school has been eliminated" deviates from this page's judgment: convergence is the synergistic stacking of mechanisms at different levels within the same system, not one school overwhelming others. You can combine with the "four source streams → one convergence point" relationship diagram on the page, following arrows from the four boxes on the left pointing sequentially to the central convergence point, then rightward into "intelligent systems," and finally downward leading to "EvoX access point: evolutionary computation × GPU × large models." Intelligence can be delivered as a "preset product" — first defining labels and a unique optimal output, letting the model approximate it.
- It can also be cultivated as an "environmental process" — only defining the search space and evaluation criteria, with the population discovering usable solutions through iteration. EvoX chooses the latter. To judge which category a new system belongs to, just ask one question: is its objective predefined by humans, or is it selected by the environment through continuous evaluation? 4. EvoX's architectural positioning. EvoX stands at the convergence point, taking evolutionary computation as its direct technical source, combining with contemporary engineering foundations such as GPU and large models, making continuous evolution engineering-runnable. This page only lays the groundwork for source streams and positioning; details such as the tensorized foundation, method evolution, and MetaDE are left for subsequent pages.

**Keyword Coverage**

- Symbolism, Connectionism, Cybernetics, Evolutionary Computation, Convergence Point, Preset Product, Environmental Process, EvoX Access Point.

**Exercise Handling**

Ask immediately after covering the five multiple-choice questions, focusing on probing the basis rather than just reporting answers. Question 1, the answer falls on Symbolism; if students choose incorrectly, hint "which route's keywords are rules, symbols, logic, rather than data, feedback, or population." Question 2, the correct statement is that the population generates change, evaluation and selection preserve effective structures, and no unique optimal solution is presupposed; the key difference is "who decides the answer." Question 3, the answer is to elevate continuous evolution from an algorithmic trick to a runnable system capability; you can remind that "architectural positioning" means not replacing any school, but making the evolutionary mechanism truly runnable and sustainable. Question 4, what conforms to the environmental-process view of intelligence is "only defining the search space and evaluation criteria, with the population discovering usable solutions through iteration."

Question 5, the accurate judgment is that classical routes provide representation, generation, and fitting, while evolutionary computation provides open search, adaptation, and continuous improvement. The common reason for incorrect answers is mostly reading "convergence" as "replacement," or mistaking evolutionary computation for a supervised method requiring labeled data, and one needs to return to this page's lecture for repositioning.

Scheme A (supervised learning approximating expert-annotated historical optimal decisions) versus Scheme B (only defining the search space and multi-metric evaluation criteria, with population iterative search, and GPU-parallel evaluation) for an urban instant delivery scheduling system. Question 1 compares two views of intelligence: the essence of the difference is not algorithmic complexity, but whether answers are predefined by humans or selected by the environment through continuous evaluation; EvoX is closer to Scheme B. Question 2 concerns long-term risks and responses regarding evaluation criteria: possible answers include improper metric design leading to precisely optimizing the wrong objective, population diversity declining and search stagnating after criteria solidify, metrics being gamed while produced solutions are business-unacceptable;

corresponding measures include multi-objective weighting or Pareto frontiers with periodic weight recalibration, introducing diversity maintenance and periodic reinitialization, setting hard constraints and safety checks for candidate solutions, and creating a closed loop where the actual performance of adopted solutions is fed back as evaluation criteria.

**Comprehension Check**

Ask one student to explain in their own words what problem each of the four source streams solves, then retell the difference between "preset product" and "environmental process"; the instructor fills in missing keywords based on the answer, especially "evaluation and selection" and "no unique optimal solution presupposed."

> **Transition Hint**
>
> After confirming students can articulate the relationship between the four source streams and the convergence point, proceed to the next page on the specific elaboration of method evolution and the tensorized foundation.

<a id="lecture-page-4"></a>

### Expansion of Evolutionary Boundaries by Large Models and Computational Power

*Page 4*

> **Page Objective:** Learners should understand how GPU parallelism and large models jointly expand the space for candidate generation and exploration.

> **Page Objective**
>
> Understand how GPU parallel computing power and large model semantic generation jointly constitute the dual engineering foundation of evolutionary computation, and expand candidate boundaries from numerical parameters to programs, model structures, and algorithmic mechanisms.

> **Lecture Sequence**
>
> First present two overarching questions — computational power determines "how many candidates can be explored at once," while large models determine "what forms of candidates can be explored"; then explain the two foundations separately, and finally explain the complementary relationship, three-level expansion, and costs.

**Key Teaching Points**

- Traditional population evaluation is a CPU serial loop, with scheduling overhead accumulating with scale. Tensorized batch evaluation organizes candidates, evaluation results, and algorithm states into tensors that can be computed as a whole, making simultaneous exploration of tens of thousands of candidates routine. It solves throughput, does not judge candidate quality, and is the underlying support for candidate space expansion. The value of large models is not in computing faster, but in understanding code, domain knowledge, and task context, capable of generating candidates with structurally complex forms such as code snippets and surrogate model topologies, thereby opening up the morphological space of candidates. Numerical parameters → program/model structure → algorithmic mechanism. The first two levels are driven by the generation capability of large models, while the third level incorporates "how to generate, how to evaluate" processes themselves into the evolutionary object, which is the prerequisite for the establishment of "using evolution to improve evolution" routes such as MetaDE and AutoPSO. Computational power expands search breadth, large models improve candidate quality and morphological complexity.
- Without either side, boundary expansion is incomplete. Evaluation costs rise, correctness verification becomes harder, and "usable answer" determination criteria become more vague, requiring integration with multi-objective trade-offs, expert decision-making, and other processes. In airfoil optimization, the search object moves up from preset numerical parameters to solver code, surrogate model structures, and even the generation and evaluation schemes themselves.
- At the same time, evaluation budget management, syntax/interface/constraint validation, failure rollback, and expert decision interfaces must be added. Treating large models as faster fitness calculators, or believing that sufficient computational power will automatically search out program-level candidates. The former loses the morphological space, the latter ignores where candidates come from.

**Exercise Handling**

Five multiple-choice questions can sequentially check computational power's role, large model capabilities, three-level expansion order, complementary relationships, and supporting capabilities. Question 1 should select "tensorized batch computation"; Question 2 should select "understanding code, domain knowledge, and context"; Question 3 should select "numerical parameters→program/model structure→algorithmic mechanism"; Question 4 should select "complementary, neither is dispensable"; Question 5 should select "evaluation cost control, correctness verification, and usable answer determination criteria." If students choose incorrectly, remind them to distinguish "computing more" from "computing correctly," and that "generating forms" and "evaluation throughput" are not the same thing.

**Comprehension Check**

Ask students to answer two questions in one sentence each — what determines how many candidates can be explored at once, and what determines what forms of candidates can be explored; then give an example of moving up from parameter optimization to algorithmic mechanism.

> **Transition Hint**
>
> After confirming students can articulate the complementary division of labor between computational power and models, proceed to the next section on specific mechanisms of method evolution.

<a id="lecture-page-5"></a>

### Multi-dimensional Conflicts and Trade-offs in Real-World Problems

*Page 5*

> **Page Objective:** Learners should understand the necessity of multi-objective conflicts and preserving differentiated candidates in real research tasks.

**Key Teaching Points**

- Points on the Pareto frontier do not dominate each other; they are only "more suitable under a certain preference." When seeing a frontier plot, first clarify what the preferences and constraints are, then discuss whether a certain solution is good.
- If the population rapidly collapses into a narrow region, first check whether selection pressure and diversity maintenance mechanisms are imbalanced. Once coverage declines, you have no switchable alternatives when constraints change.
- When real evaluations are expensive and signals are sparse, prioritize allocating budget to candidates with high uncertainty that may expand trade-off space coverage, rather than repeatedly refining around the current optimal solution.

> **Page Objective**
>
> Understand the necessity of multi-objective conflicts, Pareto trade-offs, and preserving differentiated candidates in real research and engineering optimization.
>
> Lecture Sequence and Key Points:
>
> 1. Start from conflict. Real tasks rarely have only a single objective and clear constraints; they often have multiple objectives constraining each other. For example, shortening fast-charging time brings heat generation and lifespan loss, increasing lift often accompanies increased drag, and thinning airfoils improves aerodynamics but weakens structural strength. Therefore there is no free optimum; any so-called optimum must specify "optimal under what preference."
>
> 2. Understand solutions as a set of trade-off solutions. The solution set of a multi-objective problem is the Pareto frontier. Every point on the frontier is a non-dominated solution, representing a reasonable preference orientation; without worsening other objectives, no objective can be further improved. There is no absolute ranking between points, only "more suitable under a certain preference." Dominated solutions have schemes that can comprehensively improve upon them.
>
> 3. Exploration takes priority over convergence. Premature convergence to a single point compresses the solution space into a single point, losing structural information; once constraints, costs, or risks change, there are no switchable alternatives, and one can only re-search. Population diversity should be maintained, allowing different preference directions to be sampled and evaluated simultaneously, avoiding all individuals collapsing into a narrow region of the frontier. The value of differentiation lies in responding to change.
>
> 4. The computational foundation supports multi-directional expansion. EvoX organizes candidates, evaluation results, and algorithm states into tensors that can be computed as a whole, enabling mutation, evaluation, and state updates to enter the computation graph for compilation optimization; the Runtime scheduler distributes batch computation to GPUs, acceleration devices, or distributed clusters. High-throughput batch evaluation makes maintaining large populations and covering the entire frontier engineering-feasible.
>
> 5. When evaluations are expensive and feedback is sparse, budget allocation strategies must change. Real evaluations often come from simulation, bench testing, or trial production, with high per-instance costs and sparse signals. Surrogate models should replace some high-fidelity evaluations, allocating limited budget to candidates with high uncertainty that can expand trade-off space coverage, rather than repeatedly refining around the current optimal solution.
>
> 6. Decision authority. Algorithms are responsible for expanding the trade-off space and providing a comparable set of candidates; human experts make final decisions based on scenario preferences, regulations, and business constraints. The two are complementary and cannot replace each other. In engineering, delivering a set of Pareto solutions with "preference—solution" correspondence explanations is more reliable than delivering a seemingly precise single point.

> **Material Clues**
>
> - The Pareto frontier diagram uses "lift ↑ / drag ↓" as coordinates, marking the frontier formed by non-dominated solutions, dominated solutions, diversity collapse caused by premature convergence, and parallel sampling along different preference directions. During the lecture, you can first have students judge which points in the diagram might be on the frontier, then explain that decision-makers need to select points according to scenario constraints.

**Exercise Handling**

Q1 The correct option is "the frontier is composed of a set of mutually non-dominated trade-off solutions, each point corresponding to a reasonable preference orientation." You can probe: if a point can still be comprehensively improved, it is not on the frontier. Q2 The correct option is "premature convergence loses structural information of the solution space, making it impossible to quickly switch alternatives when constraints or preferences change." Emphasize that this is an information problem, not merely a computational power problem. Q3 The correct option is "covering a broader trade-off space with limited evaluation budget, and using surrogate models to replace some high-fidelity evaluations." Q4 The correct option is "algorithms expand the trade-off space and provide a candidate set, while human experts make final decisions based on preferences and constraints." Q5 The correct option is "increasing lift typically accompanies increased drag, thinning airfoils benefits aerodynamics but weakens structure, requiring trade-offs."

Objective conflicts: fast charging generates heat and accelerates lifespan degradation; preferences change: different vehicle models, battery systems, fast-charging pile power, and warranty policies change the weights of charging time and lifespan; real evaluations are expensive and sparse: delivering a solution set covering the frontier allows reuse of evaluation budget. Operational procedure: first conduct a small number of high-fidelity evaluations and parameter calibration, construct base strategies and perturbation samples; train and validate surrogate models; enable diversity maintenance and multi-directional sampling in EvoX, directing supplementary evaluations to sparse, high-uncertainty regions based on frontier coverage; output candidate strategy groups and a preference—solution correspondence table;

finally, vehicle and battery engineers select based on range, fast-charging power, warranty, safety regulations, and cost, and perform small local refinements.

**Comprehension Check**

Have students explain in their own words "why multi-objective optimization delivers a set of solutions rather than a single point," and give an example illustrating the value of this solution set when preferences change. If students only remember "objective conflicts," prompt them to add "Pareto frontier," "diversity is information content," and "evaluation budget allocated to coverage boundaries."

<a id="lecture-page-6"></a>

### Core Ideas and Leaps in Method Evolution

*Page 6*

> **Page Objective:** Learners should understand the theoretical breakthrough of incorporating candidate generation methods into the evolutionary object.

**Key Teaching Points**

- The criterion for method evolution is simple: do the optimization variables include the method, operator, or strategy that produces candidates? If only solutions are changing, it is still traditional optimization; only when the method layer also receives feedback and updates is it method evolution.
- The solution layer answers "is this candidate good," while the method layer answers "how to produce better candidates." The two layers are connected through evaluation signals; do not mistake method layer updates for fine-tuning individual solutions.
- Method evolution does not pursue single-run optimality, but cross-task generalization, robustness, diversity, and computational cost. When reviewing, use "a solver that is better at finding solutions" to test whether you truly understand.

> **Page Objective**
>
> Make "the method for producing candidate solutions" itself an optimization object, transforming algorithms from fixed tools into continuously evolving solvers.
>
> Lecture Sequence and Key Teaching Points:
>
> 1. First locate the leap point. The traditional path is "problem→fixed algorithm→candidate solutions," where the algorithm is pre-selected by researchers and unchanged during the search process, with the optimization object being solution x. Method evolution rewrites the path as "problem family/environment→evolvable method space→candidate generator→candidate solution evaluation→method update→regeneration," with optimization variables expanded to methods, operators, hyper-heuristic rules, or candidate generation processes m. Here "method" can be specific to Differential Evolution's mutation strategy, Particle Swarm's inertia weight and topology, local search operators, or can go up a level to algorithm selection and scheduling rules.
>
> Evolutionary objects expand from solutions, parameters, and structures to methods, operators, hyper-heuristic rules, or candidate generation processes; the problem is restated as "evolving solvers that are better at finding solutions in tasks or environments"; methods produce candidates and receive task evaluations, with evaluations driving method mutation, selection, and recombination, forming continuous feedback between the method layer and solution layer; evaluation criteria shift from single-run optimality to cross-task, cross-environment generalization, robustness, diversity, and computational cost; the researcher's role shifts to designing evolvable method spaces, feedback signals, and selection mechanisms.
>
> 3. Focus on explaining the feedback loop. The method layer generates candidate generators, and the solution layer uses them to produce candidates on specific tasks and obtain evaluations; evaluations return to the method layer through aggregation or credit assignment, driving the method's own mutation, selection, and recombination. If feedback is too sparse or only rewards short-term optimality, the method layer easily overfits to a single task; only when feedback includes cross-task generalization and diversity can it learn meta-strategies that are "better at finding solutions." This can be understood as opening another layer of evolutionary search on the method space, but its evaluation cannot be separated from the solution layer's real performance.
>
> MetaDE makes Differential Evolution's strategies, parameters, and even operator combinations evolvable objects; AutoPSO incorporates Particle Swarm's structure, parameters, or topology into automatic search. Both share the same starting point: incorporating candidate-producing methods into the optimization object to achieve algorithm self-evolution. This page first establishes unified concepts; specific architectures and algorithm structure evolution will be elaborated later.
>
> If methods remain fixed, the system is still finding solutions under a given algorithm; only when the method layer also receives feedback and updates does it enter method evolution. The solution layer answers "is this candidate good," while the method layer answers "how to produce better candidates." The two layers are connected through evaluation signals; do not mistake method layer updates for fine-tuning individual solutions.

> **Material Clues**
>
> - Can be explained with the conceptual structure diagram on the page — the left side is the traditional single-path chain, the right side is the method evolution closed loop, with "leap: methods become optimization objects" in the middle highlighting the difference.
> - The bottom labels MetaDE / AutoPSO as implementation directions. Exercise Clues and Comprehension Check: The concept question should select the theoretical breakthrough of "incorporating candidate generation methods into the evolutionary object." Correct answers indicate understanding consistent with the outline.
> - For incorrect answers, first review core concepts and try again. When judging statements such as "algorithms transform from fixed tools into continuously evolving solvers," the most reasonable choice is one that can explain this page's concepts with evidence or examples and can articulate the basis.
> - Avoid skipping materials and reciting directly, concluding based only on feelings, or changing all factors simultaneously for comparison. A multi-task combinatorial optimization platform introduces method evolution. Problem restatement: from "finding the optimal solution for each task under a fixed genetic algorithm" to "evolving solvers that are better at finding solutions in a task family or environment." The evolvable method space can include crossover and mutation operator types and strengths, selection strategies, local search operators, hyper-heuristic rules, parameter control strategies, candidate generation processes, as well as MetaDE-style strategy combinations and AutoPSO-style topology and parameter structures. Feedback loop: the method layer samples or mutates a set of methods or operator configurations, the solution layer generates candidates on multiple tasks and computes fitness.
> - Cross-task performance is aggregated into feedback signals, returning to the method layer for selection, mutation, and recombination, updating method distributions, then entering the next round of generation. Evaluation shift: not only looking at single-task single-run optimality, but cross-task generalization, robustness, diversity, computational cost, and stability. Main risk: meta-layer overfitting to a small number of tasks or short-term rewards, causing method space diversity collapse.
> - Responses include task sampling, regularization, diversity maintenance, and hierarchical validation.

**Classroom Activity**

Have students retell in their own words the chain "problem family/environment→evolvable method space→candidate generator→candidate solution evaluation→method update→regeneration," and give examples distinguishing the method layer from the solution layer. Randomly ask one student to summarize the key points of this page, with the instructor filling in missing keywords.

> **Transition Hint**
>
> After confirming students can state the criterion — whether optimization variables include the method, operator, or strategy that produces candidates, and whether the method layer receives feedback and updates — proceed to the next page on specific architecture, applications, or summary.

**Keyword Coverage**

- Method Evolution, Meta-Evolution, Evolvable Method Space, Candidate Generator, Method Layer and Solution Layer, Feedback Loop, MetaDE, AutoPSO, Cross-task Generalization, Robustness, Diversity, Computational Cost.

<a id="lecture-page-7"></a>

### Inner-Outer Dual-Layer Evolution and Adaptive Mechanisms

*Page 7*

> **Page Objective:** Learners should master the dual-layer collaboration mechanism of inner-layer task solving and outer-layer strategy optimization.

**Key Teaching Points**

- When memorizing, first ask: who is solving problems? Who is changing methods? The inner layer outputs solutions and performance, the outer layer outputs configurations and strategies. Then connect "inner-layer performance → outer-layer fitness → inner-layer configuration update" into a closed loop.
- Outer-layer fitness evaluates the performance of a certain set of inner-layer configurations on batch tasks, not the optimal objective value of a single task. Understanding this prevents mistaking the outer layer for another direct solver.
- If inner-layer evaluation variance is large, the outer layer may mistake configurations that happen to perform well as truly better. When reviewing, think about how average performance, robust statistics, and increasing evaluation samples can help the outer layer make stable decisions.

Page Objective

This page centers on inner-outer dual-layer evolution and adaptive mechanisms, requiring learners to understand that method evolution is not just optimizing solutions, but adding an evolvable meta-layer control above the task-solving layer, enabling the system to automatically adjust solving methods according to task and environment changes. After completion, students should be able to clearly explain the division of responsibilities between inner and outer layers, how the feedback loop forms, and the role of GPU parallelism in it.

Lecture Sequence

Who is solving problems? Who is changing methods? This leads to the inner layer outputting solutions and performance, and the outer layer outputting configurations and strategies. Then connect "inner-layer performance → outer-layer fitness → inner-layer configuration update" into a closed loop, helping students grasp the dual-layer structure as a whole.

Key Teaching Points

1. The inner layer is the task-solving layer. It is responsible for solving actual tasks, executing large numbers of specific tasks in parallel, outputting candidate solutions, objective values f, and runtime performance such as resource consumption, convergence steps, and success rate. The memory anchor is "getting the problem solved."

2. The outer layer is the method evolution layer. It does not directly solve the original task, but evolves the inner-layer algorithm's configurations, strategies, and parameters, including evaluation, selection, mutation, and update operations. The memory anchor is "making the problem-solving method better."

3. The feedback loop is the key to the dual-layer structure. The performance of inner-layer batch tasks is aggregated into outer-layer fitness signals, used for selecting, mutating, and updating better inner-layer configurations; the updated configurations then return to the inner layer, forming a closed loop.

4. The adaptive mechanism is embodied in the outer layer continuously adjusting inner-layer strategies as tasks and environments change. It is not simply chaining two algorithms together, but adding evolvable meta-layer control above the task-solving layer, shifting the system from manually setting algorithms to algorithms automatically adapting to tasks.

The inner layer outputs "solutions," while the outer layer outputs "better methods for producing solutions." Outer-layer fitness is not the optimal value of the original task itself, but an evaluation of a certain set of inner-layer configurations' performance on batch tasks.

GPUs can run large numbers of inner-layer tasks in parallel, providing more sufficient statistical feedback for outer-layer evolution and reducing search waiting costs. It should be emphasized here that parallelism improves statistical support and efficiency; it does not cancel evaluation, nor does it guarantee global optimality.

The inner-layer DE handles specific optimization tasks, while the outer layer selects and adjusts DE configurations and strategies based on inner-layer runtime performance, such as mutation strategy, crossover rate, and population size; large numbers of inner-layer task results in turn guide outer-layer configuration selection, forming a dual-layer closed loop of "using evolution to improve evolution."

Material Clues

The left side is the inner-layer task-solving layer, the right side is the outer-layer method evolution layer. The left side outputs candidate solutions, objective values, and runtime performance, entering the right side through "performance data → fitness signals"; the right side, after evaluation, selection, mutation, and update, returns to the left side through "updating inner-layer configurations and strategies." The bottom labels GPU parallelism, used for batch running of inner-layer tasks.

Comprehension Check

How does inner-layer performance affect the outer layer? If students only say "running tasks in parallel," remind them to add "aggregated into fitness signals, then driving selection, mutation, and update."

Exercise Handling

This page's exercises center on division of responsibilities, feedback loops, the role of parallelism, and "using evolution to improve evolution."

The inner layer's main responsibility is solving actual tasks, generating candidate solutions, objective values, and runtime performance data. Emphasize that the inner layer first produces solutions and performance, then the outer layer evolves configurations and strategies.

The outer layer does not directly solve the original task because its responsibility is evolving the inner-layer algorithm's configurations, strategies, and parameters, belonging to meta-layer control. Grasp "how methods evolve," not "what the solution is."

Inner-layer evaluation results are aggregated into outer-layer fitness signals, used for selecting, mutating, and updating inner-layer configurations. The key to the dual-layer structure is the feedback loop, not open-loop parallelism.

The most important role of GPU parallelism for large numbers of inner-layer tasks is providing more sufficient statistical feedback and reducing search waiting costs. Parallelism improves statistical support and efficiency; it does not equal canceling evaluation or guaranteeing global optimality.

"Using evolution to improve evolution" means the outer layer evolves inner-layer algorithm configurations and strategies, while inner-layer performance feeds back into outer-layer fitness, forming a bidirectional closed loop.

When correct, affirm that students can connect this page's concepts and explain the basis; when incorrect, guide them back to the two judgment points of division of responsibilities and feedback loops.

Case Handling

DE solves on specific scheduling instances, producing candidate solutions, objective values, convergence steps, and other runtime performance. Outer-layer responsibility: not directly scheduling production, but evolving DE configurations and strategies, such as mutation strategy, crossover rate, and population size. Feedback loop: inner-layer batch task performance is aggregated into outer-layer fitness signals, based on which the outer layer selects, mutates, and updates better inner-layer configurations; updated configurations then return to the inner layer.

If inner-layer evaluation variance is large, outer-layer fitness can use average performance over multiple repetitions or batch tasks, median, robust statistics, or penalized lower confidence bounds to reduce misselection caused by high variance; when necessary, increase the number of evaluation samples per configuration. GPU parallelism can batch-run large numbers of inner-layer tasks, expanding sample size and shortening waiting time, making outer-layer fitness estimation more stable.

Transition Hint

After confirming students can state "the inner layer outputs solutions and performance, the outer layer outputs configurations and strategies, and inner-layer performance feeds back into outer-layer fitness," proceed to the next page's applications, examples, or summary.

<a id="lecture-page-8"></a>

### Typical Method Evolution Implementation: MetaDE Architecture

*Page 8*

> **Page Objective:** Learners should understand how MetaDE delegates Differential Evolution's mutation strategies and parameters to outer-layer optimization.

**Key Teaching Points**

- The outer layer searches algorithm configurations, the inner layer searches problem solutions. When you see "strategies, crossover methods, F, CR being optimized," that is the outer layer; when you see "is this solution good," that is the inner layer. To judge which layer any description belongs to, first ask whether it optimizes configurations or solutions.
- Inner-layer single runs carry randomness, so directly comparing two configurations with it easily yields accidental conclusions. Before comparison, let configurations run independently multiple times and take the mean (or median), then hand it to the outer layer for selection; otherwise what the outer layer selects is random noise.
- Evaluation cost approximately equals outer-layer population size × generations × inner-layer repetitions. Calculate this account clearly first, then decide investment in parallelization, tensorization, and runtime scheduling; otherwise the outer layer has not converged yet, and time and compute budgets are already exhausted.

[Page Objective] Understand how MetaDE delegates Differential Evolution's mutation strategies and key parameters to outer-layer evolutionary optimization, thereby shifting "how to evolve" from manual presetting to adaptive search.

[Lecture Sequence] First clarify the dual-layer structure (outer layer searches algorithms, inner layer solves problems), then explain the outer-layer evolutionary objects and validation loop, then compare with fixed DE and explain costs and prerequisites, and finally give a minimal example and positioning.

[Key Teaching Points]

The outer-layer evolution layer treats one DE algorithm configuration as one individual, performing selection, crossover, mutation, and elimination on the configuration space; the inner-layer task layer is Differential Evolution's conventional solving process on the original optimization task. The outer-layer optimization object is algorithm configuration (determining "how to search"), while the inner-layer optimization object is problem solutions (determining "how well it searches"). Can be explained with a diagram: the outer layer includes strategies rand/1, best/1, current-to-best/1, crossover methods, F, CR, etc.; candidate configurations are dispatched, and real performance is returned.

One outer-layer individual encodes an entire set of algorithm behavior, including mutation strategy, crossover method, scaling factor F, crossover probability CR, population size, etc. Differences between individuals correspond to the behavioral tendencies of the same algorithm family under different problem characteristics.

Each candidate configuration must be sent into the original task for actual running and be tested by real performance; the outer layer selects, eliminates, and retains based on inner-layer feedback fitness. This forms an iterative loop of "configuration generation→task validation→feedback retention/elimination." Outer-layer fitness can only come from inner-layer real running results, not solely from meta-metrics or prior judgment.

Traditional DE's strategies and parameters are preset by researchers and fixed throughout; MetaDE delegates this decision authority to outer-layer evolution, giving the algorithm adaptive capability to problem characteristics — for example, preferring more exploratory rand/1 and larger F in early search, then shifting to faster-converging best/1 and smaller F later.

Each candidate configuration must fully run inner-layer tasks once (usually also requiring multiple independent runs and averaging to suppress randomness), and evaluation cost multiplies with outer-layer population size and generations. Evaluation budget approximately equals outer-layer population size × generations × inner-layer repetitions. Therefore MetaDE strongly depends on engineering foundations such as parallelization, tensorization, and runtime scheduling.

An individual in the outer-layer population is encoded as "rand/1 + binomial crossover + F=0.5, CR=0.9." This configuration is sent to inner-layer tasks for multiple independent runs, with average fitness taken as the outer-layer individual's fitness; based on this, the outer layer selects, mutates, and retains better configurations, forming an iterative loop of "configuration generation—task validation—feedback retention/elimination." This step is the minimal runnable form of "using evolutionary algorithms to improve evolutionary algorithms."

MetaDE is the concrete implementation of "inner-outer dual-layer evolution and adaptive mechanisms" moving from idea to realizable architecture, and also provides a methodological paradigm for subsequent algorithm structure evolution (AutoPSO, EvoGO).

Differential Evolution's algorithm configuration, including mutation strategy, crossover method, and control parameters), the reason it must actually run (Answer: only real running results can serve as reliable fitness basis for outer-layer selection; prior judgment easily becomes inconsistent with actual performance), the key change from fixed DE (Answer: strategies and control parameters are no longer fixed throughout, but dynamically determined by outer-layer evolution), the main source of computational cost (Answer: each candidate configuration must fully execute inner-layer tasks once, and evaluation cost multiplies with outer-layer scale and generations), and the relationship with subsequent work (Answer: MetaDE provides a grounded paradigm of "using evolutionary algorithms to improve evolutionary algorithms," offering methodological reference for subsequent algorithm structure evolution).

If answered incorrectly, first return to the division of "outer layer searches configurations, inner layer solves problems," and the definition of the "validation loop." Two questions in the case problem: The first question's key point is that there is no reliable consistency guarantee between meta-metrics or prior experience and actual task performance; inner-layer real feedback must be used as the basis for selection, otherwise configurations that seem reasonable but perform poorly in practice will be retained, and the evolutionary direction will be systematically biased;

The second question's key point is to first estimate the total evaluation count budget (outer-layer population size × generations × inner-layer repetitions), appropriately compress outer-layer population size or generations, prioritize ensuring inner-layer repetitions are not below the level that can stably estimate the mean (e.g., 5 to 10 times), while increasing parallelism, and if necessary use early stopping or surrogate models for pre-screening, but any candidate entering final comparison and determining retention/elimination must still undergo real validation on the original task. Feedback language can reference: when correct, affirm that they can connect this page's concepts and explain the basis; when incorrect, prompt them to return to this page's lecture, find key evidence or examples, and then judge.

[Comprehension Check] Randomly ask one student to summarize MetaDE's dual-layer structure and validation loop in their own words, with the instructor supplementing missing keywords based on the answer, such as "outer layer optimizes algorithm configurations," "inner layer optimizes problem solutions," "candidate configurations must really run once," and "fitness can only come from real running results."

[Transition Hint] After confirming students can state "the outer layer searches algorithm configurations, the inner layer searches problem solutions" and the closed loop of "configuration generation→task validation→feedback retention/elimination," proceed to the next page's examples, applications, or summary.

<a id="lecture-page-9"></a>

### Algorithm Structure Evolution: AutoPSO and EvoGO

*Page 9*

> **Page Objective:** Learners should understand the leap from parameter optimization to algorithm structure evolution.

**Key Teaching Points**

- When encountering a new method, first locate its outer-layer search space: if it searches numerical configurations, it is parameter tuning; if it searches component combinations and candidate generation methods, it is structure modification. This questioning order lets you judge the method's level in one sentence.
- No matter how wide you expand the search range for learning factors or population size, the structure remains fixed, behavioral forms will not change, and it is essentially still at the parameter level. The criterion is "whether component combinations are variable," not "whether the value range is larger."
- Structure-level evolution has a larger search space and more expensive evaluations per instance, so outer-layer selection must rely on sufficiently dense "structure—performance" comparison samples. Before starting, ask yourself: is there batch parallel capability to evaluate large numbers of inner-layer tasks simultaneously? Without this, structure evolution is easily drowned by runtime noise.

> **Page Objective**
>
> This page focuses on "algorithm structure evolution." Learners should understand the leap from parameter optimization to algorithm structure evolution, and be able to use "outer-layer search space level" as a criterion to distinguish parameter tuning from structure modification.
>
> Lecture Sequence and Key Teaching Points:
>
> 1. First compare MetaDE with AutoPSO / EvoGO. MetaDE's outer-layer search space is the inner-layer algorithm's configurations and strategies (learning factors, mutation probability, selection pressure, etc.), belonging to the parameter level: structure is fixed, only numerical values are tuned, and what changes is behavioral strength. AutoPSO / EvoGO's outer-layer search space is the algorithm structure itself: which components participate in combinations, what the candidate generation process looks like, and how states are updated. What is variable at the structure level is components and connection methods, and what changes is behavioral form. The key to judgment is not the method's name, but what the outer layer is actually searching.
>
> Classical PSO's velocity—position update formula is hard-coded. AutoPSO decomposes it into a composable component pool, including velocity terms, neighborhood topologies, perturbation terms, restart strategies, etc.; the outer layer evolution then selects and assembles them into new candidate generators. In this way, "update structure" itself becomes a searchable variable. Emphasize: tuning learning factors only changes behavioral strength, while changing component combinations changes behavioral form.
>
> Focus on connecting historical feedback into the structure evolution path. Past search trajectories and performance records are no longer only used for "selecting solutions," but for determining "where the structure should change next." Feedback shifts from a result filter to a steering wheel for structure updates — what it changes is the direction of the next round of structure candidates, not picking results from a batch of completed candidates.
>
> Both share the same skeleton — the inner-layer algorithm runs on real tasks, and the outer layer evolves structures based on inner-layer performance. The key prerequisite is sample density: GPU parallel batch running of large numbers of inner-layer tasks makes "structure—performance" comparison samples sufficiently dense, so that outer-layer selection has statistical significance and will not be swayed by single-run noise. Engineering implication: algorithms shift from "one-time prior selection" to "objects that can be continuously improved"; the cost is a larger structure-level search space and higher evaluation costs, hence dependence on the tensorized parallel foundation, which is also the direct motivation for the next section's GPU parallelism topic.

> **Material Clues**
>
> - Left side component pool (composable operators), middle structure assembly (structure selection driven by historical feedback), right side candidate generator (instantiated by outer-layer structure).
> - Bottom inner-layer task evaluation and GPU parallel batch running, with feedback arrows sending inner-layer performance back to the outer layer. During the lecture, you can go through the sequence of "combination—instantiation—evaluation—feedback."

**Exercise Handling**

This page has 5 multiple-choice questions. Question 1's correct option is "whether the outer-layer search space is the inner-layer algorithm's configurations and strategies, or the algorithm structure itself"; feedback key point: searching numerical values = parameter tuning, searching component combinations and candidate generation methods = structure modification. Question 2's correct option is "decomposing Particle Swarm Optimization into a composable component pool, with the outer layer evolution selecting and assembling them into new candidate generators." Question 3's correct option is "from only being used to 'select solutions,' to being used to determine 'where the structure should change next.'" Question 4's correct option is "making 'structure—performance' comparison samples sufficiently dense, so that outer-layer selection has statistical significance." Question 5's correct option is "recombining velocity terms, neighborhood topologies, perturbation terms, and restart strategies into new candidate generators."

For each question, correct answers can affirm that students can connect this page's concepts and explain the basis; incorrect answers prompt them to return to this page's lecture to find key evidence or examples.

Case Problem Handling:

The case requires upgrading a fixed-structure PSO solver into a structure-evolvable system. Question 1 criterion: look at the outer-layer search space level. If only tuning inertia weight, learning factors, population size, iteration count, and other numerical values, it is still at the parameter level; only when the outer layer can change component combinations and candidate generation methods, such as re-selecting and assembling velocity terms, neighborhood topologies, perturbation terms, and restart strategies to change behavioral form, does it cross into the structure level. Operational test: freeze all numerical hyperparameters, allow the outer layer only to change component combinations; if search behavior still undergoes fundamental changes, it has upgraded to the structure level.

Organize the inner-layer run records after each structure instantiation into structured feedback features (component combinations, convergence trajectories, fitness curve shapes, failure or stagnation patterns, etc.), letting these features directly drive outer-layer structure update rules, such as determining "which component to modify next, which connection to add or remove," rather than simply ranking and taking the best. Difference from "only used for selecting solutions": when only used for selecting solutions, feedback is an after-the-fact result filter; in EvoGO-style usage, feedback is an ex-ante direction controller. Also remind that it needs to be paired with GPU parallel batch running of large numbers of inner-layer tasks to accumulate sufficiently dense comparison samples.

**Keyword Coverage**

- AutoPSO, EvoGO, MetaDE, Parameter Level, Structure Level, Component Pool, Candidate Generator, Historical Feedback, GPU Parallelism, Structure—Performance Comparison Samples. Comprehension Check and Classroom Activity: How to judge whether a method belongs to parameter tuning or structure modification? What do AutoPSO and EvoGO each do? You can also have students retell with an experience-near example the questioning order of "what the outer layer is searching."

> **Transition Hint**
>
> After confirming students can state the difference between the parameter level and structure level, and structure evolution's dependence on parallel sample density, proceed to the next page on GPU parallelism content or subsequent application summary.

<a id="lecture-page-10"></a>

### GPU Parallelism and Using Evolution to Improve Evolution

*Page 10*

> **Page Objective:** Learners should master how to use GPU parallelism to make algorithm self-optimization an executable computational process.

> **Page Objective**
>
> Understand how, in the dual-layer architecture of "using evolution to improve evolution," GPU parallelism converts inner-layer evaluation throughput into outer-layer usable statistical feedback, making algorithm self-optimization an executable process.

> **Lecture Sequence**
>
> First build intuition for the dual-layer closed loop — inner-layer batch execution and scoring, middle-layer aggregating performance, outer-layer selecting, mutating, or updating configurations; then explain why GPU parallelism is irreplaceable; finally use common pitfalls and exercises to test whether students truly distinguish the responsibilities of the two layers.

**Key Teaching Points**

- 1. Dual-layer structure. The inner layer is responsible for batch running large numbers of candidate algorithm configurations on GPUs, such as different hyperparameters, operators, or search strategies, and completing running and scoring for each task instance. Task granularity is small and quantity is large, perfectly matching batch throughput characteristics. The middle layer aggregates the raw performance of massive inner-layer tasks into statistical metrics, estimating the advantages and disadvantages of each configuration over the entire task distribution, forming fitness for outer-layer use. The outer layer takes statistical feedback as input to perform selection, mutation, or update, generating the next generation of configurations and sending them back to the inner layer, constituting a closed loop. Parallel throughput → performance feedback → configuration selection. The higher the parallelism, the more sufficient the feedback samples, the more stable the outer layer's fitness estimation, and the more reliable the direction of configuration improvement.
- Improved configurations are then sent back to the bottom layer, entering the next round of parallel throughput. 3. GPU parallelism is irreplaceable. Under traditional serial loops, the wall-clock time per iteration is jointly determined by "number of candidate configurations × number of task samples," becoming unbearable at slightly larger scales; serial loops thus become the main bottleneck limiting configuration space and task sample size. GPU parallelism lets large numbers of inner-layer algorithms run simultaneously, reducing serial loops from the main bottleneck to acceptable batch processing overhead, thereby simultaneously amplifying the explorable configuration space and task sample size.

> **Material Clues**
>
> - The closed-loop diagram shows task performance and fitness feedback from bottom to top, and configuration dispatch from top to bottom.
> - Each unit represents one parallel run and score of one algorithm configuration on one task instance. During the lecture, emphasize that the two data flow directions are opposite but neither is dispensable. The inner layer is "algorithms run tasks and produce scores," the outer layer is "evolution picks configurations"; do not confuse the two layers. Feedback can only come from the inner layer's real running performance, not outer-layer random guesses or artificially set constants. Whether outer-layer fitness is stable depends on the sample size provided by inner-layer parallelism.
> - The more sufficient the parallelism, the more stable the estimation.
> - When samples are on the low side, first check inner-layer throughput, rather than rushing to adjust outer-layer parameters.

**Exercise Handling**

Question 1's correct option is "performance data aggregated after inner-layer tasks run in parallel on GPUs." If students choose incorrectly, return to the closed-loop chain and emphasize: feedback must come from the real running results of large numbers of inner-layer tasks, not random numbers or artificial labels.

Question 2's correct option is "the wall-clock time per iteration accumulated by large numbers of candidate configurations and task samples in serial loops." If students choose incorrectly, hint that the bottleneck is evaluation throughput, not operator definitions, distribution approximations, or interpretability — issues unrelated to computational scale.

Question 3's correct option is "inner-layer parallel execution and evaluation → performance feedback aggregation → outer-layer configuration selection or mutation." If students choose incorrectly, emphasize that data is first produced by the inner layer, then aggregated into feedback, and finally used by the outer layer for configuration selection; the order cannot be reversed; the two layers must exchange information for it to work.

Question 4's correct option is "insufficient statistical feedback, increased outer-layer fitness estimation noise, and slower convergence." If students choose incorrectly, explain that insufficient parallelism does not equal system shutdown, but makes statistical feedback sparser and variance larger, thereby slowing or even misleading outer-layer search.

Question 5's correct option is "one parallel run and score of one algorithm configuration on a specific problem instance." If students choose incorrectly, remind them that the unit of inner-layer tasks is one execution and score of "configuration × problem instance," and thousands of such tasks running in parallel form throughput.

Question 1 requires clearly explaining that the inner layer batch-runs algorithm configurations on GPUs and completes scoring, metrics are aggregated into statistical feedback to estimate fitness, and the outer layer accordingly performs selection, mutation, or update and sends back the next generation of configurations, forming the closed loop of "parallel throughput—performance feedback—configuration selection." Question 2 should combine throughput and feedback quality: CPU serial execution limits the number of candidate configurations and task sample size by single-thread throughput, and the wall-clock time per iteration rapidly becomes unbearable as scale grows; feedback samples decrease and variance increases, making the outer layer's judgment of configuration quality unstable, search direction prone to deviation, and convergence slower or even stagnant. GPU parallelism, by improving inner-layer throughput, both expands the explorable configuration and task scale and ensures the statistical quality of feedback.

**Comprehension Check**

Ask students to retell in their own words "parallel throughput → performance feedback → configuration selection," and point out what the inner and outer layers are respectively responsible for. If students only remember that GPUs are fast but do not clearly state that feedback comes from real inner-layer running, return to the closed-loop diagram for supplementary explanation.

> **Transition Hint**
>
> After confirming students can distinguish the responsibilities of the two layers and state that feedback quality depends on inner-layer parallel sample size, proceed to the next page's applications, examples, or summary.

<a id="lecture-page-11"></a>

### Designing a Dual-Layer Optimization Experiment Blueprint

*Page 11*

> **Page Objective:** Learners should design an experimental scheme through practice that treats the Differential Evolution mutation factor as an outer-layer evolutionary object.

**Key Teaching Points**

- Without a head-to-head comparison between fixed F = 0.5 and classical adaptive DE, any "outer layer wins" result is just noise. Baselines must use the same evaluation budget, the same hardware, and the same random seed set as your scheme.
- Inner-layer evaluation is inherently noisy: run repeatedly and observe mean, median, and worst-case quantiles simultaneously, reporting confidence intervals when necessary; separate training tasks from validation tasks to avoid the outer layer merely memorizing a few lucky seeds.
- Every mapping from gene to runtime configuration must be written as directly executable rules, and recorded together with the experiment along with seeds, budgets, task IDs, hardware, and stopping conditions. Only when others can replay exactly according to these records is the blueprint truly established.

> **Page Objective**
>
> Elevate the Differential Evolution (DE) mutation factor F from a fixed hyperparameter to an outer-layer evolvable object, and design an executable, replayable dual-layer optimization experiment blueprint.
>
> Lecture Sequence and Key Teaching Points:
>
> 1. First present the dual-layer closed loop. The outer layer is responsible for evolving F strategies, while the inner layer uses a certain F configuration to run DE solving tasks and feeds task performance back to the outer layer. The outer layer updates configurations based on feedback, forming a cycle of "configuration → execution → evaluation → update."
>
> 2. The inner layer must be made into a repeatable experiment. After selecting benchmark functions or tasks, lock the DE main structure, dimensions, evaluation budget, and random seed set, with the only changing quantity being the F configuration passed in by the outer layer. Each run records optimal value, convergence generations, success rate, constraint violation, hypervolume, candidate diversity, and other metrics; these are all the information the outer layer can see.
>
> 3. What the outer layer searches is not a numerical value, but an F strategy. Common encodings include constant F, staged constants (such as [F1,F2,F3] switched by generation intervals), and scheduling functions controlled by a small number of genes (such as triggered by success rate, population variance, or generation proportion). Strategy-type encodings must clearly specify input features and output rules.
>
> 4. The mapping from genotype to runtime configuration must be explicit and reproducible. For example, [F_start, F_end, decay] can map to F(t)=F_start+(F_end−F_start)×e^(−decay×t/T), where t is the current generation and T is the total generations; [F1,F2,F3] can map to three-stage constants. The same genotype must always yield the same runtime configuration; otherwise the "good genes" learned by the outer layer cannot be explained or easily transferred.
>
> 5. Closed-loop execution depends on batch evaluation. After the outer-layer population generates a batch of F configurations, the scheduler dispatches inner-layer DE tasks in parallel, performs tensorized batch evaluation on GPUs, aggregates metrics, and then enters outer-layer selection, mutation, crossover, and update. Seeds, budgets, task IDs, hardware models, and stopping conditions need to be recorded so others can replay under the same conditions.
>
> 6. Evaluation aggregation must be robust. The inner layer is inherently noisy, so it can run independently multiple times, reporting mean, median, worst-case quantiles, or confidence intervals; at the same time distinguish training tasks from validation tasks to avoid the outer layer only remembering a few lucky seeds.
>
> First use low-fidelity models or surrogate evaluation for rough screening, then conduct high-fidelity re-evaluation of a few candidates; baseline protection: fixed F=0.5 and classical adaptive DE must be compared head-to-head, and ablation experiments must confirm that gains come from outer-layer evolution itself, not from more evaluation counts. If the outer-layer search space is too small, dual-layer optimization degenerates into manual parameter tuning, and the encoding space should be expanded.

**Keyword Coverage**

- Dual-layer Optimization, Outer-layer Evolution, Inner-layer Evaluation, F Strategy, Explicit Mapping, GPU Batch Evaluation, Baseline, Ablation, Reproducibility, Noise Protection.

> **Material Clues**
>
> - The page uses a closed-loop diagram of "outer-layer loop—inner-layer tasks—configuration mapping—task feedback" to display the blueprint, and lists required fields: inner-layer task objectives, outer-layer strategy objects, F mapping method, evaluation metrics, parallelism and budget, baseline/ablation/stopping conditions. Exercise Clues and Answer Guidance: Question 1's correct option is "fix the DE main structure, dimensions, evaluation budget, and random seed set." The key point is that only when other factors are locked can differences caused by different F configurations be attributed to F. Question 2's correct option is "continuous scheduling F(t) with exponential decay over generations." The three genes [F_start, F_end, decay] respectively control the starting point, endpoint, and decay rate. Question 3's correct option is "the scheduler dispatches inner-layer DE tasks in parallel, and performs tensorized batch evaluation on GPUs." Configurations must first be executed and measured before outer-layer fitness can be updated. Question 4's correct option is "multiple independent runs aggregated by median, worst-case quantiles, etc., while distinguishing training tasks from validation tasks." This is the key to handling inner-layer noise and preventing overfitting to lucky seeds. Question 5's correct option is "head-to-head comparison with baselines such as fixed F=0.5 and classical adaptive DE, and ablation experiments to confirm the source of gains." Only comparisons under the same budget, hardware, and seed set are valid. The case exercise requires giving mapping rules and evaluation aggregation schemes. Continuous scheduling or staged constants can be designed, but they must be explicit and reproducible.
> - The inner layer records optimal value, convergence generations, success rate, and variance; the outer layer can aggregate into rankings, convergence terms, and variance penalties.
> - Use fixed F and adaptive DE as baselines, then run an ablation group that "turns off outer-layer evolution and only randomly samples the same number of configurations" to verify that gains do not come from increased evaluation counts.

**Classroom Activity**

Have students work in groups to design a dual-layer optimization experiment blueprint that treats the DE mutation factor as an outer-layer evolutionary object, and draw the closed loop of outer and inner layers on the whiteboard; each group should write at least the genotype-to-F mapping rules, evaluation metrics, outer-layer fitness aggregation method, baseline, and ablation scheme.

**Comprehension Check**

Randomly ask students to explain in two sentences "why everything except F must be fixed" and "why explicit mapping is the prerequisite for reproducibility." If students can connect attribution, transferability, and replay, they have mastered the core of this page.

> **Transition Hint**
>
> After confirming students can draw the closed loop of "configuration mapping → inner-layer execution → task feedback → outer-layer update," and state the roles of baselines and ablation, proceed to the next page's examples or summary.

<a id="lecture-page-12"></a>

### Sustainable Development of Intelligent Systems and Environmental Challenges

*Page 12*

> **Page Objective:** Learners should understand the long-term environmental adaptation challenges that systems driven by large models need to address.

**Key Teaching Points**

- Compress sustainable development into a mnemonic: candidate generation → real environment validation → screening (accept / reject) → achievement preservation. If any link is missing, even strong intelligent components can only remain at "single-point capability."
- Rising benchmark scores only indicate that candidates are better on old data, not that they are more adapted to new environments. Whether a change is worth retaining depends on its performance in the real environment, not its ranking on a replay set.
- Ask yourself: does my system preserve and reuse validated achievements? Has it ever rejected a modification that would break existing capabilities? If both answers are "no," then it has not truly begun "sustainable development."

Page Objective

This page discusses the sustainable development of intelligent systems and environmental challenges. The core is not to continue asking how strong a single intelligent component is, but to understand that strong intelligent components are only materials and must be organized into a system framework capable of long-term environmental adaptation.

Key Teaching Points

1. Strong components do not equal sustainable systems. Larger models, stronger generation and reasoning capabilities, and higher benchmark scores do not automatically bring long-term adaptation. Connecting intelligent components to a system is only the first step.

Candidate generation, real environment validation, change screening, achievement persistence. Candidate generation requires continuously producing genuinely different strategy, structure, or parameter candidates; if candidates are highly homogeneous, evolution loses its raw material. Real environment validation lets candidates enter real tasks and real feedback, exposing distribution drift, boundary conditions, and long-tail scenarios that offline metrics cannot see. Change screening must accept effective changes and reject modifications that would break existing capabilities; the latter is often the main source of long-term degradation. Achievement persistence preserves validated effective schemes, strategies, or experience for reuse by subsequent candidate generation, forming an accumulable development trajectory.

Offline metrics only describe performance on past data, while in real environments task distributions drift, resource constraints change, and new requirements continuously pour in. Optimizing only within preset objectives or offline metrics makes the system spin on closed proxy objectives; when the environment changes, changes that have not undergone real validation may quietly erode existing capabilities. Therefore screening and memory are equally critical.

Expand from what intelligent components can do to how systems organize sustainable development and respond to real environmental challenges. Without the four types of mechanisms, even strong intelligent agents may repeat mistakes in long-term tasks or break original capabilities during updates; once the four types of mechanisms form a closed loop, strong capabilities can be stably accumulated.

Material Clues

The diagram can highlight the connection from strong intelligent components to the sustainable development system framework, with the external environment forming continuous pressure through task drift, resource constraints, and new requirements pouring in, and closed-loop arrows corresponding to batch candidates, real feedback, screened changes, and accumulated reuse.

Common Misconceptions

Mistaking a one-time benchmark score improvement as the system having completed evolution. Score improvement only indicates candidates are better on old data; whether it is worth accepting and preserving still requires real environment validation and screening.

Exercise Clues

The most accurate is to establish a system organization framework capable of responding to long-term environmental changes, letting candidates undergo real validation and be screened, accumulated, and preserved. Multiple-choice Question 2: real validation is necessary because offline optimization cannot expose how candidates fail in real environments. Multiple-choice Question 3: the core role after validation is to accept effective changes, reject modifications that break existing capabilities, and persistently preserve validated achievements. Multiple-choice Question 4: without the four types of mechanisms, the most likely outcome is long-term repeated mistakes or breaking existing capabilities during updates. Multiple-choice Question 5: the focus shifts from what intelligent components can do to how systems organize sustainable development and respond to environmental challenges.

When diagnosing a production environment automated handling system, it should be pointed out that it lacks candidate generation, real environment validation, change screening, and achievement persistence, and explain the consequences respectively: strategy homogeneity leads to repeated failures on similar faults, offline replay cannot expose production drift, destructive updates are fully deployed, and experience is not preserved so new sessions start from zero. Improvement schemes can use shadow traffic or canary channels for real validation, set up change gates, and establish an experience library to preserve validated schemes and applicable conditions; also explain the trade-offs: canary deployment and strict gates reduce evolution speed and deployment efficiency and increase costs, requiring a balance between exploration speed and non-degradation of existing capabilities.

Comprehension Check

Does my system preserve and reuse validated achievements? Has it rejected modifications that would break existing capabilities? If both are no, the system has not truly begun sustainable development.

Transition Hint

After confirming students can retell the four-stage closed loop and its necessity, proceed to the next page's examples, applications, or summary.

<a id="lecture-page-13"></a>

### Quick Start and Simplified Environment Configuration

*Page 13*

> **Page Objective:** Learners should understand how to quickly access the EvoX framework through concise tools and interfaces.

**Key Teaching Points**

- After setting up the environment, first use a minimal example to confirm that the chain of "problem → algorithm → running → results" is unobstructed, then switch to real experiments. Many problems that appear to be "algorithm not converging" actually come from missing dependencies or devices not being correctly invoked.
- Immediately after installation, verify whether dependency versions and target device types (CPU / GPU / cluster) match. The earlier environment problems are exposed, the lower the repair cost; waiting until errors occur midway through long-running experiments wastes far more time than upfront validation investment.
- Make algorithm and problem modules depend only on standardized interfaces, and do not hard-code specific devices or parallel scales in code. This way, when expanding from a single machine to a distributed cluster, often only Runtime scheduling configurations need adjustment, without rewriting algorithm logic.

Page Objective

How to complete EvoX installation and initialization with an AI-assisted minimalist process, and connect underlying computational resources with the evolutionary environment through standardized interfaces, letting researchers return to "designing problems" as quickly as possible.

Lecture Sequence

First point out the core contradiction — when actually doing experiments, what is encountered first is often not algorithm problems, but environment problems such as package installation, dependency alignment, device selection, and error localization. Then give the four-step minimal process "install → configure → validate → run," then introduce the decoupled design of standardized interfaces, and finally consolidate with common misconceptions and exercises.

Key Teaching Points

Complete environment initialization, clarifying whether this experiment uses CPU, GPU, or cluster; validation: check whether dependencies are complete and whether the target computing device can be recognized and invoked; running: run through the minimal example, confirming that the chain of "problem → algorithm → running → results" is complete. After completing the four steps, what is obtained is a trustworthy baseline, which can be used for comparison when subsequent anomalies occur, judging whether the problem lies in the environment or experimental design.

2. AI-assisted minimalist access. AI tools can compress multi-step installation, dependency pulling, and basic configuration operations into one or two natural language instructions, and provide targeted repair suggestions when version conflicts, missing dependencies, or unavailable devices occur. Its value is not in doing research for researchers, but in lowering the framework access threshold and compressing non-research engineering overhead.

3. Standardized interfaces. Algorithms, problem modules, and workflows are written only for unified interfaces, and whether the underlying layer is GPU, local multi-core, or a distributed cluster is left to Runtime scheduling. When changing hardware or adjusting parallel scale, there is usually no need to rewrite algorithm code, and the coupling cost between algorithms, problems, and computational power decreases significantly.

4. Common misconceptions. Understanding "minimal configuration" as "no need to care about the underlying layer" is the easiest pitfall to fall into. Minimalism only delegates repetitive labor to tools; whether devices and versions match, and whether computational power is truly scheduled, still needs to be confirmed in the validation stage. The simpler the configuration, the more one should develop the habit of "first run through the minimal example, then begin formal experiments"; otherwise environment problems may erupt midway through long-running experiments, at a cost far higher than upfront validation.

Classroom Activity

Have students retell this page's process in their own words, and give an example of a situation where "it appears the algorithm is not converging, but actually dependencies are missing or the device is not being correctly invoked," to test understanding of the role of the validation stage.

Exercise Handling

This page's 5 multiple-choice questions center on the value of AI tools, the purpose of standardized interfaces, key points of the validation stage, the role of minimal examples, and single-machine to cluster migration strategies. When handling, first have students choose answers, then probe the basis. Question 1's correct option is "compressing multi-step installation, dependencies, and configuration operations into a small number of instructions, and giving repair suggestions when errors occur," guiding students to distinguish engineering configuration from algorithm design and runtime scheduling. Question 2 should select "algorithms, problems, and computational power," emphasizing that the interface layer sits between the evolutionary environment and underlying computational resources. Question 3 should select "whether dependencies are complete and versions match, and whether the target computing device can be recognized and invoked," reminding that validation targets the environment and resources themselves.

Question 4 should select "used to confirm that the end-to-end chain among installation, configuration, device invocation, and result output is connected," as the minimal example establishes a trustworthy baseline, not a performance benchmark. Question 5 should select "keeping algorithm and problem modules basically unchanged, only adjusting Runtime scheduling and device configuration," because horizontal scaling only changes scheduling configuration, not algorithm and problem code. Affirm correct answers and guide incorrect answers back to the corresponding paragraphs to find evidence.

Case Handling

"Completing EvoX experiment environment deployment within one week" requires designing a minimalist onboarding process and explaining the role of AI tools. It is recommended to unfold in four steps: installation: AI generates EvoX and dependency installation instructions based on the machine environment and unifies versions; configuration: AI combines Python version and device type to generate basic configuration, clarifying use of a GPU workstation; validation: dependencies are complete, versions compatible, and GPU can be recognized and invoked; running: the minimal example runs through the end-to-end chain. The role of AI tools runs through all four steps: translating multi-step operations of consulting documentation and troubleshooting one by one into a small number of executable instructions, and giving repair suggestions when errors occur.

The second question concerns how standardized interfaces reduce coupling costs. The key point is that algorithms are written only for unified interfaces, and which specific GPU, how many local processes, or which cluster nodes execute them is determined by Runtime, so migration usually only changes scheduling and device configuration, without rewriting algorithm and problem code. Before migration, verify: the new environment has consistent dependency versions with the original environment, the target device can be recognized and invoked, data distribution and inter-node communication are normal, and rerun the end-to-end chain with the minimal example under the new topology, while recording performance before and after migration to avoid misjudging environment differences as algorithm improvements.

Comprehension Check

Randomly ask one student to summarize this page's key points in their own words, with the instructor supplementing missing keywords based on the answer, such as "four-step minimal process," "standardized interfaces," "decoupling," "trustworthy baseline," and "first run through the minimal example."

Transition Hint

After confirming students can state the two relationships — "minimal configuration does not equal not caring about the underlying layer" and "write algorithms above the interface, schedule computational power below the interface" — proceed to the next page's examples, applications, or summary.

<a id="lecture-page-14"></a>

### Researchers' Core Competencies and Mindset Shift

*Page 14*

> **Page Objective:** Learners should understand the shift in researchers' core responsibilities from writing code by hand to asking key questions in AI-assisted research.

**Key Teaching Points**

- Before each experiment, first ask yourself: is the level of this change parameters, programs, algorithm structure, or the research process? If the level is unclear, all subsequent results will be difficult to explain.
- Metrics, proxy signals, acceptance thresholds, failure criteria. Setting only one primary metric is the most common hidden danger: you neither know "how good is good enough" nor "how bad should be discarded," so conclusions naturally cannot withstand scrutiny.
- Are baselines at the same level? Are budgets equal? Are seeds rerun multiple times? Are computational conditions consistent? Are statistical tests appropriate? Imagine: if you change a random seed, does your conclusion still hold?

> **Page Objective**
>
> Understand that in AI-assisted research, researchers' core responsibility shifts upward from "writing implementations by hand" to "asking key questions, defining evaluation criteria, and making critical judgments about evidence." This page uses a competency radar chart and three types of key questions to illustrate the concrete landing points of this mindset shift.
>
> Lecture Sequence and Key Teaching Points:
>
> In the past, capability was measured by "whether one could write an implementation by hand"; today code and experiment scripts can be quickly produced by AI, and what is truly scarce is asking key questions, defining evaluation and fair comparison criteria, and making critical judgments about AI-produced evidence. The radar chart compares "traditional researchers" and "AI Native researchers": the former emphasizes coding implementation, while the latter stands out more in problem definition, evaluation design, fair comparison, critical judgment, and scientific taste, echoing "AI implements answers, humans control direction."
>
> After implementation costs are lowered, the researcher's role shifts upward from "implementer" to problem definer, experiment designer, and evidence arbiter. The problem definer judges which types of problems have scientific value; the experiment designer turns vague ideas into executable, reproducible, comparable experimental protocols; the evidence arbiter remains skeptical of curves, metrics, and conclusions, probing credible boundaries and failure conditions.
>
> 3. Three types of key questions that must be answered by humans:
>
> Is what needs to be changed optimization parameters, program implementation, algorithm structure, or the research process itself? Different levels make conclusions completely different in meaning.
>
> Metrics, proxy signals, acceptance thresholds, and failure criteria — none of the four can be missing. Setting only one primary metric easily leads to neither knowing "how good is good enough" nor "how bad should be discarded."
>
> Whether baselines, budgets, randomness, computational conditions, and statistical tests are aligned. Misaligned comparisons often misread random fluctuations or computational advantages as algorithmic progress.
>
> AI can generate large numbers of "runnable" schemes, but feasible does not equal valuable. Taste matters — judging among many feasible schemes which problem, which hypothesis, and which piece of evidence truly has scientific value. The center of gravity of capability shifts from coding speed to critical thinking, system abstraction, domain judgment, and human-machine division of labor.
>
> EvoX / MetaDE-type tasks. AI can generate dozens of mutation operator variants and accompanying experiment scripts in a day, but researchers must decide whether to improve mutation operators, optimize the evaluation process, or reconstruct the search space, and accordingly set multi-objective metrics and fair baselines; otherwise what is obtained is only "runnable" results, not trustworthy knowledge.
>
> Mindset Shift, Problem Definer, Experiment Designer, Evidence Arbiter, Change Object, Evaluation Feedback, Fair Comparison, Taste matters, AI implements answers/humans control direction.

> **Material Clues**
>
> - Radar chart six dimensions — coding implementation, problem definition, evaluation design, fair comparison, critical judgment, scientific taste.
> - On which dimensions are the differences between traditional researchers and AI Native researchers concentrated?

**Exercise Handling**

The correct option is "from 'implementer' to 'problem definer, experiment designer, and evidence arbiter.'" Feedback key point: the implementation link can be largely undertaken by AI; what is scarce is judging which problems are worth answering and which evidence is worth trusting.

Is it optimizing parameters, rewriting programs, adjusting algorithm structure, or reconstructing the research process?" Feedback key point: first ask the level of change, then discuss whether results are good or bad; if the level is unclear, it is impossible to explain whether improvements are credible.

The correct option is "metrics, proxy signals, acceptance thresholds, and failure criteria." Feedback key point: the four respectively define observation methods, approximate signals, passing standards, and failure conditions; none can be missing.

The correct option is "baselines, budgets, randomness, computational conditions, and statistical tests." Feedback key point: these are control variables for fair comparison; misalignment will misread random fluctuations or computational advantages as algorithmic improvements.

The correct option is "judging whether to change the mutation operator, evaluation process, or search space, and accordingly setting multi-objective metrics and fair baselines." Feedback key point: directional and evaluative judgment reflects scientific taste, which is what turns "runnable" into "trustworthy knowledge."

Case Study Handling:

Parameter level (mutation strength), operator level (replacing mutation operators), structure level (reconstructing the algorithm backbone), or process level (evaluation and selection process). Evaluation feedback: primary metrics and proxy signals (such as hypervolume, number of evaluations required for convergence), acceptance thresholds (improvement relative to baseline), failure criteria (significantly worse than baseline or discarded if variance is too large).

Same baseline, consistent evaluation budget; fixed and repeatedly rerun random seeds; same computational conditions; appropriate statistical tests (such as rank-sum tests). Consequences of neglect: misreading random fluctuations or computational dividends as algorithmic progress, ultimately obtaining only "runnable" results.

**Comprehension Check**

Ask students to answer in one sentence — "Is the level of this change parameters, programs, algorithm structure, or the research process?" — and explain why if the level is unclear, subsequent results are difficult to explain. You can also have students retell the four-piece evaluation feedback set and the fair comparison self-check list.

> **Transition Hint**
>
> After confirming students can distinguish the three types of key questions — change object, evaluation feedback, and fair comparison — and understand the upward shift of the researcher's role, proceed to the next page's further elaboration on examples, applications, or method evolution.

<a id="lecture-page-15"></a>

### Building Sustainable Evolutionary Infrastructure

*Page 15*

> **Page Objective:** Learners should understand EvoX's positioning as infrastructure connecting the real world with long-term evolution.

> **Page Objective**
>
> Understand that EvoX is not a one-time solver, but open infrastructure connecting the real world with long-term evolution.

> **Lecture Sequence**
>
> First compare "one-time solver" with "long-term infrastructure." Traditional optimization faces static benchmarks, running once and ending upon obtaining a solution; the real world, however, continuously changes, with objective drift, updated constraints, high evaluation costs, and delayed feedback, thus requiring a foundation that can run long-term, be reused, and be extended.
>
> Connect the real world, accessing real environments and feedback; support long-term evolution, continuously generating candidates, validating effects, and accumulating improvements; face complex changing problems, handling dynamic, multi-objective conflicts and continuously explored scenarios; openness and sustainability, carrying algorithms, problems, runtime, and feedback mechanisms as public capabilities.
>
> Continuous running—validation—retention—re-evolution. The system continuously generates candidates and deploys them to real environments, the environment returns evaluation signals, effective candidates and improvements are retained and accumulated, and when objectives and environments change, new exploration continues to be scheduled on the same foundation.
>
> Connect high-cost engineering simulations or robot tasks to EvoX, continuously receive evaluation signals, and retain effective candidates; algorithms, operators, runtime, and feedback mechanisms do not need to be rebuilt from scratch, but expand and are maintained like urban pipeline networks.

**Key Teaching Points**

- The learning focus should be on interfaces, scheduling, feedback loops, and achievement accumulation mechanisms, rather than fixating on one algorithm. If any step is missing, the system degenerates into a one-time solver and cannot accumulate long-term improvements. When the environment or objectives change, first check whether problem modules and feedback mechanisms can be reused, then decide whether to replace algorithms, avoiding rebuilding the entire pipeline from scratch. A diagram can be used to emphasize the cyclic relationship among the real environment, EvoX open infrastructure, algorithms and runtime scheduling, and long-term improvement achievement accumulation.

> **Exercise Clues**
>
> - Multiple-choice questions center on infrastructure positioning, closed-loop order, connecting the real world, reuse advantages when environments change, and openness and sustainability features. When giving feedback, grasp: the real world continuously changes.
> - The closed loop starts from continuous running.
> - Real feedback must be accessed, not just simulation.
> - When changes occur, continue scheduling and reuse on the same foundation.
> - Public capabilities should be extensible, reusable, and sustainable. Simulators and robot experiment platforms are connected as real environments, continuously generating control strategy candidates, collecting evaluation signals such as energy consumption, precision, and adaptability, retaining effective improvements, and entering a new round of evolution. If the objective becomes multi-objective optimization balancing precision and energy consumption, then the algorithm layer is replaced with a multi-objective evolutionary algorithm, the problem module modifies objective functions and constraints, the feedback mechanism expands evaluation channels, and runtime scheduling continues to run on the same foundation.

**Comprehension Check**

Ask students to explain in their own words "why infrastructure is needed rather than a solver," then retell the four steps of the closed loop. If students can state that EvoX is a foundation, the closed loop can accumulate, and when changes occur, first reuse then replace algorithms, proceed to the next page.

<a id="lecture-page-16"></a>

### Summary and Outlook: Toward Higher-Order Intelligent Forms

*Page 16*

> **Page Objective:** Learners should review the overall mechanism of method evolution and understand its significant importance in driving intelligent systems toward self-evolution.

> **Page Objective**
>
> Review the overall mechanism of method evolution, understand how it drives intelligent systems from "solving answers" to "improving methods," and toward higher-order autonomous evolutionary forms.

**Key Teaching Points**

- This page can be grasped with one mnemonic — "solution space → method space." The criterion is: is the object currently being iterated "solutions," or "the method for producing solutions"? As long as the answer becomes the latter, it has entered method evolution. Inner-layer evolution searches for solutions to a given problem in the solution space, pushing the current method to its limit, producing candidate solutions and evaluation results.
- Outer-layer evolution uses task feedback to improve solving methods in the method space, with search objects becoming algorithms, operators, or structures. The closed-loop direction is: task feedback points from inner to outer, method updates return from outer to inner. In this way, solution quality becomes the outer layer's evaluation signal, and improved methods return to the inner layer, forming a continuous closed loop from solution space to method space. For method evolution to truly land, populations, evaluation results, and algorithm states need to be organized into tensors that can be computed as a whole, and heterogeneous computational power scheduled on this basis. Through tensorized computation and a unified foundation architecture, EvoX decouples "how to evolve" from "how to efficiently schedule," giving method evolution and system-level continuous evolution an engineering landing foundation, driving intelligent systems from "solving answers" to "improving methods," and even to higher-order autonomous evolution. MetaDE. MetaDE uses inner-layer Differential Evolution (DE) to solve problems and outer-layer evolution to search DE configurations or structures, letting "the solving method itself" participate in iteration. What it searches is not the solution to a specific problem, but the mechanism that produces solutions, which is precisely the intuitive embodiment of crossing from solution space to method space. A higher-order intelligent form is not about obtaining the optimal solution once, but about whether it can continuously produce stronger methods and stronger system capabilities. The key to the next step is making the inner-outer closed loop more stable and more transferable, and supporting the AI Native research paradigm on foundations like EvoX. Common Misconception: The inner layer searches "solutions to a given problem," while the outer layer searches "algorithms, operators, or structures." Task feedback direction is inner→outer, and method update direction is outer→inner. Do not confuse method evolution with ordinary parameter tuning. If the system only seeks better solutions within a fixed solution space and does not change the solving method itself, it does not belong to method evolution. When unsure whether a system belongs to method evolution, see whether it is also searching for "the method that produces solutions." If so, it is doing method evolution.
- Otherwise it is just ordinary parameter tuning.

> **Exercise Clues**
>
> - Let solving methods themselves, such as algorithms, operators, structures, or strategies, become objects that are iterated and improved. If answered incorrectly, distinguish "finding better solutions in the solution space" from "letting the method itself participate in iteration." The inner layer searches for solutions in the solution space, while the outer layer uses task feedback to improve solving methods, i.e., the method space. Feedback direction is inner→outer, and update direction is outer→inner. Provide tensorized computation and a unified foundation, giving method evolution and system-level continuous evolution an engineering landing foundation.
> - The key is organizing populations, evaluations, and states into tensors that can be computed as a whole, and decoupling "how to evolve" from "how to schedule." Use outer-layer evolution to search DE configurations or structures, letting the solving method itself participate in iteration, thus embodying the leap from solution space to method space. It should be described as continuously producing stronger methods and stronger system capabilities, rather than only pursuing a single-run optimal solution. When answers are correct, emphasize that they can connect this page's concepts and explain the basis.
> - When answers are incorrect, return to the page's lecture, find key evidence or examples, and then judge. Build an optimization system that "can self-improve" for production scheduling problems. The inner layer uses DE to search scheduling schemes, and the outer layer uses task feedback as signals to search DE's operator configurations, parameters, and structures. The role of task feedback is to translate solution space performance into method space improvement directions: if solution quality improves, retain and strengthen the current method configuration.
> - If solution quality stagnates or degrades, explore new operator combinations or structural adjustments. To evaluate whether it is moving toward higher-order autonomous evolution, three types of indicators can be set: method-layer indicators check whether the method is continuously improved.
> - Transferability indicators check adaptation performance on unseen tasks.
> - Autonomy indicators check whether, under low human intervention, it can autonomously discover effective operator combinations or structural adjustments, with a reproducible process. Combining all three, if the method continuously strengthens, is transferable, and can self-improve with low intervention, the system can be judged to be moving toward a higher-order autonomous evolutionary form.

**Comprehension Check**

Ask students to retell in their own words the meaning of "solution space → method space," and choose any system to judge whether it is doing method evolution.

> **Transition Hint**
>
> After confirming students can clearly explain the division of labor between inner and outer layers, the closed-loop direction, and the comparative significance of MetaDE, proceed to the next page's examples, applications, or summary.