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

Class Hours: 1 · Slide Pages: 16

## Reading Navigation

- [Lesson 2 | Topic: Origins of Evolutionary Computation and the Convergence of Multiple Routes](#lecture-lesson-1)

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## Lesson 2 | Topic: Origins of Evolutionary Computation and the Convergence of Multiple Routes

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### Origins of Evolutionary Computation and the Convergence of Multiple Routes

*Page 1*

> **Page Objectives:** Trace the intersection points of symbolism, connectionism, cybernetics, and evolutionary computation, revealing how evolutionary computation provides a solution path that does not depend on a predetermined unique answer.

> **Page Objectives**
>
> Evolutionary computation is not an isolated technique, but a class of solution paradigms formed at the intersection of multiple AI research traditions. Learners should be able to articulate the connections among symbolism, connectionism, cybernetics, and evolutionary computation, and gain a preliminary understanding of why evolutionary computation is suited to handling conflicting objectives and complex constraints.

> **Lecture Sequence**
>
> Begin with a question-driven introduction—if a task has no unique standard answer, or has multiple competing objectives, what difficulties arise with the traditional approach of "first define the optimal solution, then solve for it"? This leads to the divergence and convergence of routes in AI history: symbolism emphasizes symbolic reasoning and explicit knowledge, connectionism emphasizes learning representations from data, cybernetics focuses on feedback, adaptation, and system regulation; evolutionary computation pushes these threads toward the path of "population search, mutation, selection, and iterative improvement." Then clarify the main thread of this lesson: in complex scenarios, rather than relying on a predetermined unique answer, candidate solutions gradually evolve through evaluation and selection.

**Key Teaching Points**

- First, origins are not simple inheritance but convergence. Different traditions each answered different aspects of intelligence, and evolutionary computation drew from them ideas such as "adaptation," "feedback," and "population exploration." Second, the convergence of multiple routes means methods are not a single choice. In actual research and engineering, symbolic rules, neural networks, control feedback, and evolutionary search often complement each other. Third, the advantage scenarios for evolutionary computation are: when objectives may conflict, constraints are complex and difficult to write as a single analytical expression, or evaluation can only be given through simulation, experiments, human preferences, etc. In such cases, rather than pursuing a one-time derivation of a unique optimal solution, it is better to maintain a set of candidate solutions and approach better trade-offs through iteration.

**Comprehension Check**

"Why is it said that evolutionary computation provides a solution path that does not depend on a predetermined unique answer?" You may prompt them to answer from the perspectives of "multiple candidate solutions," "evaluation and selection," "gradual improvement," and "trading off conflicting objectives." If students only answer "random trial and error," supplement: the key to evolutionary computation is not blind randomness, but search with a population, selection pressure, and iterative feedback.

> **Transition Tip**
>
> Next, we will move into specific cases and methodological frameworks to observe how this line of thinking is implemented as an operable intelligent evolution system; while listening, pay attention to which parts correspond to "convergence of origins" and which parts correspond to "solving under complex constraints."

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### The Re-convergence of Multiple Intelligent Routes

*Page 2*

> **Page Objectives:** Learners should understand the convergence background of multiple exploration routes in AI history and its significance for modern intelligent systems.

**Key Teaching Points**

- Map symbolism, connectionism, cybernetics, and evolutionary computation respectively to knowledge representation, problem understanding, action feedback, and diversity preservation, then converge them into EvoX. When recalling, first think of the four origins, then the four dimensions, and finally land on the convergence point.
- Large model single-pass generation provides candidate possibilities, but does not automatically evaluate and inherit; an evolutionary loop requires environmental feedback, evaluation and selection, and long-term accumulation. To judge whether a system has completed convergence, check whether it has evaluation, selection, error correction, and inheritance.
- Do not pre-specify a unique answer, generate variation, environmental evaluation, preserve diversity. These four keywords are anchors for understanding the technical origins of EvoX, and can also help you identify the parts for which evolutionary computation is responsible in case questions.

Page Objectives

This page is the starting page of Lesson 2, "Origins of Evolutionary Computation and the Convergence of Multiple Routes," helping learners review the divergence and convergence of symbolism, connectionism, cybernetics, and evolutionary computation in AI history, understand how "the re-convergence of multiple intelligent routes" explains the technical lineage of EvoX, and distinguish the problem entry points each of the four routes faces.

Lecture Sequence

Start with "AI is not a single-line evolution," then use the structural diagram to explain what the four origins respectively emphasize, then explain the dimensions of their convergence in modern intelligent systems, and finally land on the positioning of EvoX and evolutionary computation as the most direct technical origin. During teaching, guide students to observe the diagram: the four routes on the left pass through the four dimensions in the middle and converge to EvoX on the right; the orange path emphasizes evolutionary computation.

Key Teaching Points

Emphasize explicit knowledge representation, logical reasoning, and rule constraints, suitable for explaining how knowledge is represented and manipulated.

Emphasize learning patterns and representations from data, suitable for handling high-dimensional mappings in problem understanding and perception generation.

Emphasize feedback, regulation, and action loops, suitable for explaining how a system continuously corrects behavior in an environment.

Emphasize population search, generating variation, environmental evaluation, and diversity preservation, suitable for open exploration and multi-candidate optimization, and is the most direct technical origin of EvoX.

It is not simply adding the four routes together, but letting them play complementary roles in the same intelligent system: symbolic rules provide constraints and interpretability, connectionist models provide perception and generation capabilities, control feedback provides action correction, and evolutionary search provides open exploration and diversity. In this way, the system can continuously evaluate, select, correct, and inherit amid environmental changes.

Located at the intersection of multiple routes, it is not a tool grown separately within a single route, but a practical framework that builds an evolutionary loop between model capabilities and system capabilities. Among these, evolutionary computation does not pre-specify a unique answer, but seeks balance through generating variation and environmental evaluation, while preserving diversity.

Keyword Coverage

Evolutionary computation, swarm intelligence, diversity. "Diversity" can be explained as preserving different candidate solutions and exploration directions, avoiding premature convergence to local optima, and is key to sustained adaptation in open environments.

Exercise Clues

The exercises on this page include concept discrimination and case studies. For the concept question, ask learners to judge the core understanding of "the re-convergence of multiple intelligent routes"; the correct answer should land on: understanding the convergence background of multiple exploration routes and its significance for modern intelligent systems, rather than merely memorizing four terms. In feedback, if students choose to skip the concept, prompt them to review the core concepts first and then try again.

The case study "Route Integration of an AI Native Research Platform" can be handled as follows:

Action feedback lacks a cybernetic-style loop because it does not receive real environmental evaluation; knowledge representation lacks symbolic-style explicit constraints and interpretable rules; diversity preservation lacks evolutionary-computation-style open search and multiple candidate solutions; problem understanding and sustained adaptation lack connectionist-style data-driven learning and representation updating.

An integration plan can encode hypotheses and experimental plans as mutable individuals, with evolutionary computation responsible for population search; use symbolic rules to constrain safety boundaries and logical consistency; use neural network models to estimate fitness or information gain from historical data; use a cybernetic loop to feed real experimental results back as evaluation signals; and then preserve promising solutions through selection, crossover, mutation, and inheritance. Evaluation can integrate reproducibility, information gain, resource consumption, and diversity metrics, and during selection retain a certain proportion of exploratory solutions to avoid premature convergence.

Classroom Activity

Symbolism corresponds to knowledge representation, connectionism to problem understanding, cybernetics to action feedback, and evolutionary computation to diversity preservation, then they converge into EvoX. First think of the four origins, then the four dimensions, and finally land on the convergence point.

Large model single-pass generation provides candidate possibilities, but does not automatically evaluate and inherit; the evolutionary loop requires environmental feedback, evaluation and selection, and long-term accumulation. Students can use "Does it have evaluation, selection, error correction, and inheritance?" to judge whether a system has completed convergence.

Do not pre-specify a unique answer, generate variation, environmental evaluation, preserve diversity.

Comprehension Check

When seeing a large model application, does it only do single-pass generation? Does it have environmental feedback, evaluation and selection, long-term inheritance, and diversity preservation? If not, it indicates that it has not yet completed the systematic loop of multi-route convergence. Based on the answers, supplement the missing keywords.

Transition Tip

After confirming that students can state the emphasis of the four routes and their convergence relationship toward EvoX, proceed to the next page for the specific development or application of the origins of evolutionary computation.

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### Review of Symbolism, Connectionism, and Cybernetics

*Page 3*

> **Page Objectives:** Learners should understand the emphases of different early AI routes in problem understanding and knowledge representation.

> **Page Objectives**
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> Help learners understand how the three representative early AI routes—symbolism, connectionism, and cybernetics—gave different answers to the question of "how intelligence arises," and be able to compare their knowledge carriers, computational mechanisms, and relationships with the environment using a unified coordinate system.

> **Lecture Sequence**
>
> Where does intelligence come from? Then present the three routes in sequence. Symbolism uses logical symbols as its carrier, obtaining conclusions through explicit knowledge representation and rule-based reasoning; the typical chain is "Premise A—Rule A→B—Inference Engine—Conclusion B." Connectionism uses neuronal connection weights as its carrier, extracting patterns from data through distributed parallel representation learning among input, hidden, and output layers; intelligence emerges from the coordination of a large number of simple units. Cybernetics takes the feedback loop as its core, focusing on error regulation, stability mechanisms, and goal achievement in a dynamic environment through target values, comparators, controllers, controlled objects, and feedback links.
>
> Knowledge carrier (symbols/weights/state feedback), computational mechanism (logical reasoning/parallel representation/feedback regulation), relationship with the environment (offline modeling/data-driven/online interaction).

**Key Teaching Points**

- The three routes are not mutually exclusive camps, nor does one replace another. Modern intelligent systems often combine rule constraints, neural network representations, and feedback control in the same task. For example, in an adaptive urban traffic signal control system: the rule base embodies symbolism, the traffic flow prediction neural network embodies connectionism, and the feedback controller based on queue length error embodies cybernetics. If the feedback loop is removed, the system degrades to open-loop, making it difficult to cope with sudden congestion, sensor noise, and model drift, and stability and long-term effectiveness decline. This precisely shows that the three routes are complementary, and also provides a historical framework for subsequent evolutionary computation, hybrid intelligence, and "the re-convergence of multiple intelligent routes."

**Keyword Coverage**

- Symbolism, connectionism, cybernetics, knowledge representation, explicit reasoning, connection weights, distributed parallel representation, feedback loop, error regulation, knowledge carrier, computational mechanism, environmental relationship, complementarity.

> **Exercise Clues**
>
> - Does symbolism correspond to explicit symbols and logical rules
> - Does connectionism emphasize connection weights and parallel representation learning
> - Does cybernetics take the perception-decision-execution loop and error regulation as key
> - Is the difference among the three routes in knowledge carriers symbolic structures, connection weights, and state feedback
> - Is the value of reviewing the three routes to identify complementarity and provide a framework for hybrid intelligence and evolutionary computation. In handling, first let students explain the basis for their choices, then return to the corresponding evidence in the lecture. The case question requires identifying the three routes in the urban traffic system and reasoning about the limitations of removing the feedback loop; the answer should land on open-loop systems, lack of real-time error correction, and difficulty of self-correction under environmental change and model drift.

**Classroom Activity**

Ask students to retell the three routes using a familiar scenario, such as campus crowd management, classroom temperature control, or traffic scheduling, and point out which part of the scenario corresponds to symbolic rules, which part corresponds to parallel prediction, and which part corresponds to feedback regulation.

**Comprehension Check**

How is knowledge represented? How is computation parallelized? How is behavior regulated? If students can point out in the same scenario which part belongs to which route, it indicates that they have mastered the comparative coordinates of this page.

> **Transition Tip**
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> After confirming that students can state the core differences and complementary relationships of the three routes, proceed to the next page on the origins, applications, or comprehensive discussion of evolutionary computation.

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### Evolutionary Computation as a Direct Technical Origin

*Page 4*

> **Page Objectives:** Learners should master the core characteristics and value of evolutionary computation not depending on a predetermined unique answer.

> **Page Objectives**
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> Master the positioning of evolutionary computation as a direct technical origin, understanding that it does not depend on pre-specifying a unique answer, but instead forms acceptable adaptive solutions under conflicting objectives and complex constraints through cycles of mutation, evaluation, selection, and recombination.

> **Lecture Sequence**
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> 1. Building on routes such as symbolism, connectionism, and cybernetics, point out that evolutionary computation can most directly be transformed into algorithmic systems.
>
> Do not preset a unique answer; evaluation is continuous feedback; selection is environmental pressure.
>
> Initial population → mutation or crossover generates variation → environment or evaluation function provides feedback each generation → selection and retention → new generation population; after each generation's evaluation, it flows back, cycling repeatedly. The multicolored individuals in the diagram represent diversity preservation under conflicting objectives, indicating that the system preserves different trade-off directions.
>
> Traditional single-objective optimization often requires first specifying weights, compressing multiple metrics into one objective; evolutionary computation maintains a candidate population, preserves individuals with different trade-off directions after each generation's evaluation, and ultimately gives a comparable set of solutions rather than a unique answer. Taking high performance, low cost, and strong robustness as an example, single-objective methods must first fix weights, while evolutionary computation allows individuals with different trade-off directions to survive simultaneously, leaving decision-makers to choose after seeing the costs clearly.
>
> The cycle of mutation, evaluation, and selection is the conceptual origin for subsequent systematization, engineering, and parallelization implementation, enabling dispersed intelligent components to continuously accumulate.

**Key Teaching Points**

- Intelligence is not the product of pre-specifying a unique answer, but a continuous process of generating variation, accepting environmental evaluation, and preserving and recombining. From gene accumulation to language and culture, and then to software, computing power, and AI carriers, the evolutionary cycle keeps compressing
- The "child machine" idea proposed by Turing in 1950 can be regarded as a precursor to the idea of gradually acquiring abilities in an environment through education, experimentation, and rewards and punishments. Although large models possess understanding, generation, and reasoning abilities, single-pass generation cannot self-verify correctness and is difficult to remain effective in the long term amid continuous changes in environment and models
- Evolutionary computation fills in the link of continuous feedback. It does not preset a unique answer; the algorithm generates diverse candidate individuals, which are screened by the environment or evaluation function
- Evaluation is continuous feedback; each generation provides the population with a direction for adjustment
- Selection is environmental pressure; through retention and elimination, more adapted individuals gain more opportunities to continue. Evaluation is not equal to final acceptance. Evaluation occurs in every generation and is the driving force for population updating
- Once it is treated as a one-time judgment, solving retreats to the dilemma of single-pass generation that cannot self-verify.

> **Exercise Clues**
>
> - Five multiple-choice questions are used to check conceptual positioning. The correct direction for Question 1 is to view solving as a continuous process of generating variation, environmental evaluation, and preservation or recombination
> - If answered incorrectly, remind them that the key word is continuous process, not giving an answer once. The correct direction for Question 2 is that evaluation is continuous feedback occurring in every generation
> - If answered incorrectly, ask further: if evaluation occurs only once, can the population still be guided in subsequent generations? The correct direction for Question 3 is that selection is retention and elimination under environmental pressure, allowing more adapted individuals to gain more opportunities to continue
> - If answered incorrectly, emphasize that selection is not a one-time decision, but a mechanism repeated in every generation. The correct direction for Question 4 is to maintain a candidate population, preserve individuals with different trade-off directions, and output a set of comparable solutions
> - If answered incorrectly, recall the example of performance, cost, and robustness. The correct direction for Question 5 is that the mechanisms of mutation, evaluation, and selection can be directly transformed into algorithmic systems and form adaptive solutions through iteration
> - If answered incorrectly, point out that "direct" means implementable and runnable, rather than remaining at the speculative level. A team designs an online inference system, simultaneously pursuing high throughput performance, low deployment cost, and strong service robustness. If the single-objective approach continues to be used, weights must first be specified, compressing the three metrics into one total score, and then searching for the unique optimal configuration according to the total score
> - The risk is that weights imply value judgments, which will exclude other compromise directions in advance, and when the relative importance of the three metrics changes, the weights must be reset and the search repeated. After switching to evolutionary computation, the team does not preset a unique answer, but maintains a candidate population, preserves individuals with different trade-off directions after each generation's evaluation, continuously generates new configurations through mutation and recombination, and ultimately gives a set of comparable solutions, allowing decision-makers to choose after seeing the actual costs. The key links of the loop are: initial population → mutation or crossover generates variation → environment or evaluation function scores → selection and retention → new generation population, and flows back into the next round. Evaluation occurs every generation and can be updated with the environment and constraints, so it is continuous feedback rather than final acceptance
> - Its significance lies in not needing to compress all values into a single objective, being able to explore compromise solutions among multiple metrics, and gradually approaching an acceptable balanced solution under complex constraints through feedback each generation. Remember the evolutionary loop with three beats—mutation (generate variation) → evaluation (environmental feedback) → selection (retain or eliminate). When encountering any evolutionary algorithm, first ask three things: where does variation come from, who scores it, and who is retained. Evaluation is not equal to acceptance
> - For multiple objectives, do not rush to normalize; first ask whether the solution set covers different trade-off directions, rather than only keeping the answer with the highest total score.

**Comprehension Check**

Ask students to explain in their own words why evaluation must occur generation by generation, and judge whether a solution set covers different trade-off directions. Being able to explain that after the evaluation function changes, the population distribution will move in the corresponding direction indicates that they have grasped the meaning of feedback; being able to point out that the solution set should not only retain the highest total score indicates that they understand multi-objective trade-offs.

> **Transition Tip**
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> After confirming that students can fully state the three links of mutation, evaluation, and selection, and can distinguish evaluation from final acceptance, proceed to the next page's examples, applications, or summary.

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### Is Intelligence a Preset Product or an Environmental Process

*Page 5*

> **Page Objectives:** Learners should understand that intelligent systems are dynamic processes continuously formed in the environment rather than products completed by pre-design.

**Key Teaching Points**

- The product model asks "what does it output," and capabilities are frozen before deployment; the process model asks "how does it act and how does it receive feedback," and capabilities are gradually generated during operation. When encountering a new case, first ask yourself: is the behavioral boundary of this system fixed before operation?
- Understanding intelligence as a process changes the object of design from "fixed behavior" to "a mechanism that generates and improves behavior." How actions are generated, where feedback comes from, by what standard candidates are evaluated, what is retained, and when updating occurs—none of these can be missing, and the absence of any link will stop the loop.
- To judge whether a system truly has evolutionary capability, you can ask continuously: Does the feedback signal truly exist and can it flow back into the system? Are multiple candidate groups generated simultaneously for mutual comparison? Are there clear evaluation and retention rules so that experience is no longer lost? If any of these three questions cannot be answered, it is still on the side of a "preset product."

# Is Intelligence a Preset Product or an Environmental Process

## Page Objectives

An intelligent system is not a product that is pre-designed and fixed after deployment, but a dynamic process that continuously evolves in the environment through action, feedback, screening, and retention. Understanding this distinction is the foundation for subsequently mastering the ideas of evolutionary computation.

## Lecture Sequence and Key Points

Product model and environmental process model.

Requirements → design → packaging → deployment; capabilities have their boundaries defined before operation and no longer change after deployment. The advantages are controllability, reproducibility, and ease of acceptance; the disadvantage is that when the environment changes, it can only be redesigned and redeployed, and the problem periodically repeats.

Action → environmental feedback → multi-candidate evaluation → retain the better → update strategy → act again. Capabilities are gradually generated during operation, not requiring one-time correctness, but continuously acting, receiving feedback, and turning it into a basis for adjustment.

The "child machine" idea proposed by Turing in 1950—gradually acquiring abilities in the environment through education, experimentation, and rewards and punishments—is an early form of the environmental process approach. Large model single-pass generation cannot self-verify correctness and is also difficult to remain effective amid environmental changes.

Screening and retention. Continuous adaptation in the process model relies on evaluating, screening, and retaining more effective results from multiple possible outcomes. Each round generates multiple candidate groups, the environment gives success/failure and quality signals, and the system eliminates, retains, and precipitates better paths as the starting point for the next round based on these. Retention prevents experience from being wasted and allows capabilities to accumulate.

Understanding intelligence as a process does not mean abandoning design. Action interfaces, feedback signals, evaluation standards, and retention rules must all be explicitly designed; it is only that the object of design changes from "fixed behavior" to "a mechanism that generates and improves behavior."

The focus of research shifts from "choosing or deploying a fixed answer" to "building a process that can explore, provide feedback, and evolve." The model generates possibilities, the environment provides feedback, and the system is responsible for evaluation, selection, and inheritance. To judge whether it is AI Native, look at whether it has this loop, not at how strong the model is.

## Keyword Coverage

Preset product, environmental process, action, feedback, screening, retention, multi-candidate evaluation, strategy updating, evolutionary loop, child machine, AI Native, EvoX.

## Material Clues

The page is accompanied by a "Comparison Diagram of Preset Product Model and Environmental Evolution Model." The product model on the left is a linear process: requirements → design → packaging → deployment → fixed capability, with the note below: "Capability boundaries are defined before operation and no longer change after deployment." The environmental evolution model on the right is a circular loop: action → environmental feedback → multi-candidate evaluation → retain the better → update strategy → act again, with the center marked "Evolutionary loop: feedback · screening · inheritance," and the note below: "Act in the environment and continuously adjust from feedback." This diagram can be used to intuitively compare the two models.

## Exercise Handling

The page contains five multiple-choice questions and one case to test understanding.

Key points of the multiple-choice questions:

The behavioral boundary of the system is basically determined before operation, and capabilities remain fixed after deployment. Feedback prompt: in the product model, capabilities are packaged before operation and no longer change structurally during operation; if the system can change behavior according to feedback during operation, it is closer to "process."

Rules and parameters are presets before operation, while the process model emphasizes feedback signals obtained during operation.

Accuracy, deployment cycle, and data scale are one-time metrics of the product model and do not constitute a continuous adjustment mechanism.

The keywords are "evaluation, screening, retention"; exhaustive enumeration is infeasible, manual rules cannot cover changes, and pure random attempts lack retention and accumulation.

5. Implications for EvoX and AI Native research? Correct answer: shift from choosing or deploying a fixed answer to building a process that can explore, provide feedback, and evolve. Feedback prompt: larger models, single-pass generation, and online deployment all remain within the "fixed answer" mindset, lacking a feedback-capable and evolvable loop.

The team trains a fixed grasping strategy according to the product model; after deployment, the success rate declines due to environmental changes, and they can only repeatedly recollect, train, and deploy.

Use the "preset product/environmental process" framework to explain the first-stage problem and why it repeatedly fails. Key answer points: the first stage treats intelligence as a preset product, capabilities are packaged before operation, behavioral boundaries are fixed at deployment, and the system cannot adjust according to environmental changes; when packaging material, lighting, and conveyor belt speed change, the conditions on which the original strategy depends fail, and the system can only return to the starting point and manually recollect, train, and deploy; each round of redeployment freezes the boundaries again, the problem periodically repeats, the adjustment cycle is long, and experience is difficult to accumulate.

① Action generation: each grasp generates multiple candidate poses and gripping forces; ② Feedback source: the environment gives success/failure signals (success, slipping, time consumption, damage), supplemented by manual sampling inspection; ③ Evaluation and screening: score and rank according to metrics such as success rate, time consumption, and damage rate, and eliminate invalid candidates; ④ Retention and inheritance: record high-scoring solutions and corresponding contexts as starting points for similar contexts, accumulating experience; ⑤ Strategy update and action again: use high-quality samples to update the strategy, put it into the next round, and form a continuous loop.

## Classroom Activity and Comprehension Check

Students can be asked to retell the difference between the two models using a life-related example (such as learning to ride a bicycle or recommendation algorithm adjustment), and to state the relationship between the example and "the product model asks what it outputs, while the process model asks how it acts and receives feedback." Randomly ask one student to summarize the key points of this page in their own words, and the teacher supplements missing keywords based on the answer, such as "action interface," "feedback signal," "evaluation standard," and "retention rule."

## Transition Tip

After confirming that students can state the core difference between preset products and environmental processes, and can use "whether the behavioral boundary is fixed before operation" to judge which side a system belongs to, proceed to the next page's examples, applications, or summary.

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### Complexity and Multi-Objective Conflicts in the Real World

*Page 6*

> **Page Objectives:** Learners should understand the objective reality that multiple objectives pulling against each other in the real world means there is no single optimal solution.

> **Page Objectives**
>
> Understand the objective reality that in real-world multi-objective optimization, objectives constrain each other and there is no single optimal solution, and be able to explain why evolutionary computation is suitable for handling such problems.

**Key Teaching Points**

- Improving accuracy often increases inference latency and computing cost
- Enhancing robustness may sacrifice peak performance
- Reducing energy consumption may limit model capacity. These objectives are not fully commensurable, so there is no unique solution that makes all objectives optimal simultaneously; there is only a set of non-dominated trade-off solutions, i.e., approaching the Pareto front. The horizontal axis is inference latency, and the vertical axis is computing cost
- Gray point clouds are feasible solutions, and the red boundary is the Pareto front
- Points that fall inside the front and are comprehensively no worse than other solutions and worse in at least one objective are dominated solutions. The knee point (inflection point) is a cost-effective trade-off point on the front; further improving one objective requires greatly sacrificing another objective
- The evolutionary population gradually approaches the front through parallel search. Multi-objective conflict—improving one objective often causes another to degrade
- Complex constraints—hard constraints, soft constraints, uncertainty, and dynamic environments jointly compress the feasible region, making the single-point optimal assumption invalid
- A unique optimal solution usually does not exist—scalarization or single-point search struggles to express the real decision space
- The output should be a solution set—preserve diverse candidates and provide decision-makers with a comparable trade-off boundary and preference selection space. Solution A dominates Solution B if and only if A is no worse than B on all objectives and strictly better than B on at least one objective. Evolutionary computation commonly uses non-dominated sorting and crowding distance to approach the front while maintaining convergence and diversity. It does not rely on the gradient or differentiability of the objective function; each individual in the population represents a candidate solution, simultaneously approaching multiple trade-off solutions through parallel search
- Decision-maker preferences usually enter at the final stage, selecting solutions from the front, which is more robust than locking in weights in advance.

**Exercise Handling**

The correct option for Question 1 emphasizes that objectives constrain each other and there is no unique solution that makes all objectives optimal simultaneously; scalarization loses trade-off information.

The correct option for Question 2 is "A is no worse than B on all objectives and strictly better than B on at least one objective"; remind students that dominance requires being comprehensively no worse, not merely better in one objective.

The correct option for Question 3 points out that the weighted sum method depends on prior weights, can only obtain a finite number of solutions, and may miss non-convex parts of the front.

The correct option for Question 4 is "population parallel search and diversity preservation can simultaneously approach multiple trade-off solutions"; in feedback, emphasize low gradient dependence and diversity maintenance.

The correct option for Question 5 is to find a set of Pareto trade-off solutions and then choose according to scenario preferences; simply adding or optimizing only a single objective will obscure trade-off relationships.

**Classroom Activity**

The objectives are to maximize accuracy, minimize latency, and minimize energy consumption; hard constraints include device memory limits, and soft constraints may include energy budget and bandwidth; uncertainty comes from bandwidth fluctuation, disconnection, and load changes. Because objectives conflict and are not fully commensurable, there is no unique optimal solution, and only a set of Pareto trade-off solutions can be obtained. The team can use population parallel search to retain multiple non-dominated solutions, use non-dominated sorting and crowding distance to maintain diversity and approach the front; finally select solutions by combining knee points and preference intervals, use feasibility rules or penalty functions for hard constraints, and introduce online evolution or robust optimization for dynamic environments.

**Comprehension Check**

Ask students to explain in their own words "why one should be cautious when seeing a unique optimal solution in a multi-objective problem," and give an example of how improving one objective causes another to degrade.

> **Transition Tip**
>
> After confirming that students can distinguish dominated solutions, non-dominated solutions, and knee points, and can explain why population diversity is important, proceed to the next page's application or summary.

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### Breaking Through the Contradiction Between Diversity and Engineering Cost

*Page 7*

> **Page Objectives:** Learners should understand how the mechanisms of variation and selection in nature inspire solutions to the problem of high diversity cost in traditional engineering.

> **Page Objectives**
>
> Complex structures in nature come from the continuous process of "variation—selection," and diversity is an inherent product of this mechanism; traditional engineering has long regarded diversity as an additional cost. The core shift is to reinterpret diversity from a "cost item" to a "search resource."

> **Lecture Sequence**
>
> Variation generates new candidate branches, and selection retains effective paths and eliminates inefficient paths based on environmental feedback. The generation and screening of candidates occur simultaneously, without needing to first exhaustively enumerate all possible solutions and then verify them one by one.
>
> If 100 feasible solutions are needed, it usually means about 100 serial rounds of "design—simulation—evaluation—prototyping." Each attempt consumes manpower, computing power, and time, and diversity appears as a high marginal cost; the process is naturally serial, scheduling is cumbersome, and the scale of exploration quickly hits a ceiling.
>
> Organizing candidates into a population allows simultaneous generation and simultaneous evaluation, with selection pressure uniformly applied to the parallel candidate set. Parallelism is not a later optimization technique, but an inherent property of this mechanism.
>
> Diversity changes from an "additional cost item" to a "search resource"—more candidates mean broader coverage and stronger robustness; the bottleneck shifts from serial manual scheduling to parallel computing throughput, and scalability is determined by the scale of computing power.
>
> GPU parallel computing power and large model intelligent generation capabilities provide the foundation for large-scale candidate generation and parallel evaluation, but they themselves are not selection and inheritance mechanisms; the core of the mechanism is still the loop of "mutation generates candidates, selection retains paths."

**Key Teaching Points**

- A single candidate corresponds to one iteration in a serial process, while the entire population corresponds to one parallel batch. To judge whether a method can be parallelized, look at whether its search unit is a single solution or a population that can be generated and evaluated simultaneously. GPUs and large models make large-scale parallel exploration feasible in engineering, but the evaluation and screening links can never be omitted. The natural evolutionary tree on the left shows mutation branches and retention/elimination
- The serial pipeline at the upper right emphasizes that diversity equals additional cost
- The parallel population at the lower right shows batch generation, parallel evaluation, and selection retention, and points out that the bottleneck shifts to parallel computing throughput. Evolutionary computation turns the traditional contradiction that "diversity necessarily means high cost and cumbersome serial scheduling" into a scalable parallel computing problem.

**Exercise Handling**

This page has a concept judgment question and a case question. The concept question focuses on checking whether learners can explain "diversity" as a search resource and explain why evolutionary computation is suitable for parallelism; the correct answer should connect to the simultaneous generation and evaluation of populations and the uniform application of selection pressure. The case question requires pointing out that the "diversity—cost" contradiction is concentrated in the three links of solution generation, simulation evaluation, and process scheduling; when reorganizing the process, one should organize the population for batch generation, parallel evaluation, unified selection, retention of differentiated solutions, and continued mutation, forming a loop of "generation—evaluation—selection—re-mutation," with the bottleneck shifting from manual scheduling to parallel throughput. In feedback, affirm correct answers for being able to connect to the evidence on this page; guide incorrect answers back to the comparison diagram of the serial pipeline and parallel population.

**Comprehension Check**

Why is diversity a cost in traditional engineering but a resource in evolutionary computation? If learners can say "the population can be parallelized, evaluation and selection are still necessary, and the bottleneck shifts to computing throughput," proceed to the next page.

> **Transition Tip**
>
> After confirming that learners can distinguish the "enabling foundation" from the "mutation—selection mechanism," proceed to the next page's details on how GPUs and large models support large-scale candidate generation and parallel evaluation.

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

### The Engineering Foundation Formed by GPUs and Large Models

*Page 8*

> **Page Objectives:** Learners should master the solid support provided by GPU parallel computing and large model generation capabilities for large-scale evolutionary exploration.

> **Page Objectives**
>
> GPUs provide parallel throughput, large models provide semantic entry points and context continuity, and EvoX is responsible for the closed loop of search, selection, and feedback. Learners should be able to distinguish the three layers of division of labor and judge system shortcomings accordingly.

> **Lecture Sequence**
>
> First guide students through the three-layer structure diagram. The top large model layer is responsible for natural language intent understanding, code understanding and generation, and project context continuity; the middle EvoX engineering foundation operates according to "candidate generation → parallel evaluation → feedback return → continued evolution"; the bottom GPU layer is responsible for batch parallel evaluation, high-throughput iteration, and broadening exploration boundaries. Then explain the changes after computing power is unlocked: evaluation no longer waits in queues, large batches of candidates can be sent into parallel evaluation simultaneously, and feedback returns almost simultaneously. Next, explain that candidate sources shift from manual design and small-scale sampling to a hybrid model of "parallel computing batch supply + large model generation and rewriting." Finally, clarify the boundary between foundation and algorithm, and supplement multi-agent collaboration, recursive task decomposition, and project-level persistence.

**Key Teaching Points**

- The most fundamental contribution of GPUs is throughput, advancing evaluation and search from serial, small-batch processing to large-scale simultaneous exploration
- They are not responsible for semantic understanding or context storage. The most accurate role of large models is semantic entry points and context continuity, converting research intent more quickly into executable candidates, scripts, or operator drafts, and maintaining consistency with the project context
- No matter how many candidates there are, they still need environmental feedback and evaluation selection to be screened into capabilities that can accumulate sustainably. Remember the three as a saying: GPUs handle "can it run," large models handle "can it connect," and EvoX handles "can it keep turning." When seeing a problem, first judge whether what is missing is computing throughput, a semantic entry point, or a loop mechanism, and then decide which layer to supplement.

**Keyword Coverage**

- Engineering foundation, GPU parallel computing, large models, semantic entry points, context continuity, search·selection·feedback loop, candidate generation, parallel evaluation, feedback return, continued evolution, batch candidate distribution, high-throughput iteration, broadening exploration boundaries, multi-agent collaboration, recursive task decomposition, project-level persistence, cross-agent context.

> **Material Clues**
>
> - The three-layer architecture diagram is suitable for pointing and explaining—the purple large model layer at the top, the green EvoX loop in the middle, and the blue GPU layer at the bottom. The arrows in the diagram embody "research intent → candidate drafts" and "constraint and context return," as well as "batch candidate distribution" and "parallel evaluation results." When discussing project-level persistence, "objectives, constraints, intermediate products, and evaluation results" can be treated as four project assets that must be accumulated.

> **Exercise Clues**
>
> - Five multiple-choice questions respectively check GPU responsibilities, large model responsibilities, changes in candidate sources, multi-agent and project-level persistence, and the division of labor between foundation and algorithm. The answers are: C, A, A, C, C. When handling, do not just report the answers; ask students to state the reasons for exclusion: GPUs are not semantic understanding or storage
> - Large models are not the loop itself
> - Candidate sources are expansion and hybridization, not canceling evaluation or returning to manual one-by-one design
> - Persistence allows key context to continue across different agents
> - The foundation does not replace the algorithm itself, nor does it eliminate uncertainty. Operator automatic search platform. Answer points are divided into two layers. First, the GPU layer undertakes throughput responsibility, batch parallel evaluation, and broadening the explorable range
> - The large model layer undertakes semantic entry and context continuity responsibilities, converting requirements into candidate implementations, scripts, or operator drafts. The two cannot replace each other because large models do not have the throughput for batch parallel numerical evaluation, and GPUs do not understand natural language and project semantics. Second, multi-agent collaboration and recursive task decomposition require shared project-level persistence: decompose the overall objective into subtasks such as requirement understanding, task decomposition, candidate generation, parallel evaluation, and result archiving, claimed by different agents
> - Precipitate objective definitions, constraints, candidate versions, evaluation metrics, and failure reasons at the project level. In this way, even if an agent or model version is changed midway, the new agent can continue the existing progress; after the GPU-side evaluation results are written back, they become input for the next round of candidate generation, and the loop can continue operating across contexts.

**Comprehension Check**

Randomly ask one student to summarize the three-layer division of labor in their own words, and answer "if the system performs poorly, should we first check computing throughput, semantic entry, or the loop mechanism." Then ask students to verify the loop according to "candidate generation → parallel evaluation → feedback return → continued evolution," and point out which step is missing or slowed down; if the background must be explained to the agent again every round, it is likely that project-level persistence has not been done well.

> **Transition Tip**
>
> After confirming that students can distinguish the division of labor between foundation and algorithm, proceed to the next page on specific implementation, application cases, or summary.

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

### Expansion of the Boundary of Evolutionary Objects: From Parameters to Programs

*Page 9*

> **Page Objectives:** Learners should understand how modern evolutionary systems expand evolutionary objects from numerical parameters to programs and algorithm mechanisms.

> **Page Objectives**
>
> Understand how modern evolutionary systems expand evolutionary objects from numerical parameters to programs and algorithm mechanisms, and master the four-level ladder of "numerical parameters → hyperparameters and model structure → algorithm mechanisms and operators → programs and engineering state."

**Key Teaching Points**

- For each level upward, ask three things—how are candidates represented? What constraints apply? What signal is used for evaluation? From the "value range" of the parameter level to the "syntax, semantics, and executability" of the program level. The freer the object, the heavier the verification responsibility; do not misread expansion as being able to generate code arbitrarily. If a single loss function is still used to evaluate code module-level candidates, it indicates that evaluation lags behind
- It is necessary to check whether compilation passes, whether tests cover enough, whether the benchmark improves, whether resource overhead is acceptable, and whether multi-objective trade-offs are needed. Evolutionary upgrade of a recommendation system training pipeline. Stage one, learning rate and batch size correspond to the numerical parameter level; candidates are continuous real-valued vectors and can be evaluated with a single objective on the validation set
- Stage two, network depth, width, and data augmentation correspond to the hyperparameter and model structure level; values are discrete, the combination space is large, and in addition to validation metrics, generalization and overfitting must be considered
- Stage three, loss function form and sampling operators correspond to the algorithm mechanism and operator level; candidates must satisfy syntax and semantic constraints, and evaluation should incorporate robustness, stability, and multi-objective trade-offs
- Stage four, training scripts and engineering pipeline configuration correspond to the program and engineering state level; evaluation upgrades to whether compilation and tests pass, whether resource overhead is acceptable, and the multi-objective trade-off between accuracy and cost. Core rule: the closer the object is to a program, the more evaluation must move from a single scalar to system-level feedback. In representation, abstract scripts into operable syntactic structures or modular components (pipeline stages, hyperparameter injection points, control flow blocks), rather than unparseable plain text
- In constraints, ensure compilability, consistent interfaces and dependencies, and runnability within limited time and computing budgets
- In evaluation, in addition to accuracy, include test pass rate, single evaluation cost, reproducibility, and multi-objective Pareto trade-offs. The main risks are a sharp increase in search space, high evaluation cost, many candidates that "run but yield no benefit" wasting computing power, and incorrect candidates possibly damaging engineering stability. Countermeasures include hierarchical priors, layer-by-layer opening, surrogate models or low-cost screening, and explicitly trading off accuracy and overhead.

**Exercise Handling**

The questions on this page are used to check whether one can explain core concepts and the expansion of evolutionary objects with evidence or examples. The correct direction is "can connect to the concepts on this page and explain the basis"; if choosing "change all factors simultaneously to compare results," "draw conclusions only by feeling," or "skip materials and directly recite," then it does not meet the requirements. Feedback can be given as follows: when correct, say "this understanding is consistent with the confirmed outline"; when incorrect, prompt "first review the core concepts, or return to the explanation on this page to find key evidence or examples before judging."

**Comprehension Check**

Randomly ask students to summarize the four-level ladder in their own words, and give an example to illustrate the representation, constraints, and evaluation signal of an object at a certain level. Transition Tip: After confirming that students can state "constraint transfer" and "evaluation signal matching level," proceed to the next page's examples, applications, or summary.

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

### Large Model Capability Boundaries and System-Level Continuous Evolution

*Page 10*

> **Page Objectives:** Learners should understand that although large model capabilities are strong, a system-level framework is still needed to organize their continuous development.

**Key Teaching Points**

- To judge whether a piece of work is truly advancing, first ask yourself one question: can this output be directly inherited by the next task? If the answer is no, it is likely just one-time consumption, not evolution. Memory anchor: generating possibilities relies on the model, accumulating improvements relies on the system.
- Context exhaustion, absence of ground truth, and experimental scheduling respectively correspond to precipitation and reuse, evaluation and screening, and construction and invocation of the environment. When encountering a problem that "cannot be changed," first locate which category the gap belongs to, and then decide which system capability to supplement, rather than considering changing the model first.
- The implementation of project-level continuous evolution is to solidify the output of each task into these four types of assets. Develop the habit: after a task ends, spend ten minutes writing the reusable parts back into the repository, especially recording those failed cases of "this path does not work"—they also raise the starting point of the next task.

> **Page Objectives**
>
> Understand that although large model capabilities are strong, continuous evolution is a system-level responsibility, not a natural extension of model capability. Core division of labor: the model is responsible for generating possibilities, and the system is responsible for applying selection pressure and preserving history.
>
> Key Teaching Points
>
> Semantic understanding can parse vague requirements into executable descriptions and identify constraints; generation capability can produce code, configurations, plans, and explanations within minutes, and self-revise within the same dialogue round; multi-step reasoning can complete task decomposition, chain derivation, and trade-off explanation; tool invocation connects language output to real actions through functions and external interfaces. As a result, the generation of candidates is nearly free, and what is truly scarce is the mechanism for applying selection pressure to candidates.
>
> A single session cannot close the loop. State within a session is volatile, and cross-task memory breaks after context exhaustion; ground truth is absent, and the model cannot autonomously define "what counts as correct," nor can it easily provide evaluation standards independent of its own output; experimental scheduling is absent, and batch benchmark testing, statistical significance testing, constraint compliance judgment, and high-cost real-environment execution are all outside the model's control. Therefore, the model cannot independently complete the loop of "candidate generation—environmental verification—result precipitation" within a single session or single task. This is not because capability is insufficient, but because the responsibility was never on the model in the first place.
>
> Candidate generation and mutation organize model output and actively expand and structure the search space; construction and invocation of the environment build a repeatably executable experimental environment, responsible for scheduling, execution, and acceptance; evaluation and screening define ground truth and evaluators, completing ranking, screening, and significance judgment; precipitation and reuse write successful results and failed experience into long-term memory, becoming the starting point for the next task. Without the model, the system lacks screenable diversity; without the system, the model can only reset to zero again and again.
>
> The criterion for measuring whether a system is truly evolving is not "how good a single output is," but "whether improvements can accumulate across tasks and across agent cycles." A stunning output that cannot be inherited is still one-time consumption. When each task output is solidified into reusable engineering assets—code, configurations, evaluators, failure case sets—the starting point of subsequent tasks is higher than the previous one, and this is project-level continuous evolution. EvoX's practice provides a stable and reliable engineering precipitation paradigm: across agents and task cycles, solidify experience into assets rather than leaving it in dialogue records.
>
> When encountering "improvements cannot accumulate," the first reaction is often "switch to a stronger model," but the root cause is usually architectural absence. You can self-check: if the same task is given to a stronger model, will it automatically gain cross-task memory, ground truth judgment, and experimental scheduling? If not, the problem is not on the model side. The correct action is to supplement the system, not switch the model.
>
> This page only argues "why system-level continuous evolution is needed" and does not elaborate on the algorithm's own dual-layer evolution mechanism, which is covered in the subsequent MetaDE-related content.
>
> Generating possibilities relies on the model, accumulating improvements relies on the system.
>
> Exercise Handling
>
> The model can generate candidates, but cannot independently complete the loop of "candidate generation—environmental verification—result precipitation" within a single session. If students mistakenly choose "tool invocation can autonomously complete experimental scheduling," guide them back to the latter half of the loop.
>
> Multiple-choice Question 2 corresponds to "absence of ground truth → evaluation and screening." You can prompt that the three types of gaps correspond one-to-one with system functions: context exhaustion → precipitation and reuse, absence of ground truth → evaluation and screening, experimental scheduling → environment construction and invocation.
>
> The root cause of the boundary problem is architectural absence; without the system, it is impossible to apply selection pressure and preserve history, and changing the model does not change the structure.
>
> Whether improvements can accumulate across tasks and across agent cycles, making the starting point of subsequent tasks higher than the previous one.
>
> The direct value of multiple-choice Question 5 is to let successful and failed experience enter long-term memory, raise the starting point of the next task, and avoid repeating mistakes; assets include code, configurations, evaluators, and failure case sets.
>
> The model can independently complete requirement parsing and constraint identification, generate multiple implementations and trade-off explanations, and perform multi-step reasoning and code-level self-checking; what must be undertaken by the system is candidate organization and mutation, batch benchmark environment scheduling execution and acceptance, evaluation screening and significance compliance judgment, and precipitation and reuse. Without the system, candidates cannot be compared, conclusions cannot be reproduced, experience cannot be inherited, the next time still starts from zero, and improvements reset to zero.
>
> The precipitation mechanism should include reusable code and configurations, reusable evaluators, failure case sets, and task-result experimental trajectories; its role is to let the next task start from the existing candidate space and known failure boundaries, allow evaluators to compare with historical bests, let the failure set narrow the search space, and unify the measurement standard of experimental trajectories, making improvements accumulative, auditable, and transferable.

**Comprehension Check**

Ask students to restate in their own words "the model is responsible for generating possibilities, and the system is responsible for applying selection pressure and preserving history," and give examples to illustrate the difference between one-time output and inheritable engineering assets. After confirming that students can distinguish the three types of gaps and the four types of system work, proceed to subsequent content.

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

### MetaDE: Using Evolutionary Algorithms to Improve Evolutionary Algorithms

*Page 11*

> **Page Objectives:** Learners should understand the advanced strategy of using evolutionary mechanisms to optimize evolutionary configurations through inner and outer nested layers.

**Key Teaching Points**

- The inner layer searches for "the answer to the problem," and the outer layer searches for "an algorithm that is better at finding answers." When encountering a meta-optimization problem, first ask yourself: does the individual currently being evolved represent a solution or a configuration? This question determines the subsequent encoding and evaluation methods.
- Configuration generation → task solving → performance feedback → configuration update. Remember these four steps and you have mastered the operating skeleton of MetaDE; among them, "performance feedback" is the only link connecting the two layers, and without it the outer layer cannot select.
- Each time the outer layer evaluates a configuration, the inner layer must run completely once, and the budget grows exponentially; at the same time, beware that configurations are only effective for the current task. When comparing, comparisons should be made under the same evaluation budget, and robustness of results should be tested with multiple tasks or multiple repetitions.

> **Page Objectives**
>
> Understand how MetaDE treats the evolutionary algorithm itself as an evolutionary object, allowing differential evolution to automatically adapt to tasks through inner and outer nested layers. Core sentence: the inner layer searches for "the answer to the problem," and the outer layer searches for "an algorithm that is better at finding answers."

> **Lecture Sequence**
>
> Previous evolutionary algorithms place the population in the solution space for search, and fitness measures candidate solutions. MetaDE pushes up another level, treating the evolutionary algorithm itself as the optimization object, expanding the goal from "solving the problem" to "improving the solving method."
>
> The inner layer runs differential evolution in the solution space; given a mutation strategy and key parameters such as scaling factor F, crossover rate CR, and population size NP, it solves benchmark functions or engineering tasks and produces task performance as fitness. The outer layer evolves in the algorithm configuration space; each individual encodes a set of algorithm configurations; its fitness cannot be calculated by itself and must be determined by the performance returned by the inner layer after running the task with that configuration. The outer layer then selects, recombines, and mutates accordingly.
>
> Configuration generation → task solving → performance feedback → configuration update. Four-step cycle; the algorithm adaptively improves with the task, rather than manually fixing parameters in advance. Among these, "performance feedback" is the only link connecting the two layers; without it, the outer layer cannot select.
>
> Configuration space is not equal to solution space. The inner layer searches solutions, the outer layer searches configurations; the two are coupled because the quality of the outer configuration must be measured by the actual operation of the inner layer. The general skeleton of meta-optimization is: make the search itself the object being searched.
>
> Each time the outer layer evaluates a configuration, the inner layer must run completely once, and the computational budget grows exponentially; therefore, the granularity of the configuration space and the evaluation budget must be balanced; also beware of configurations overfitting to the current task, and inner-layer random noise misleading outer-layer selection. When comparing, comparisons should be made under the same evaluation budget, and robustness should be tested with multiple tasks or multiple repetitions.

**Keyword Coverage**

- MetaDE
- inner and outer layers
- configuration space and solution space
- differential evolution
- mutation strategy
- scaling factor F
- crossover rate CR
- population size NP
- configuration generation
- task solving
- performance feedback
- configuration update
- meta-optimization
- computational budget
- overfitting
- robustness.

> **Material Clues**
>
> - The outer configuration population generates configurations and distributes them
> - The inner layer executes differential evolution according to the configuration and evaluates task performance
> - Performance feedback returns to the outer layer
> - The outer layer updates configurations. When looking at the diagram, remind students to identify the arrow directions and the four-step loop.

> **Exercise Clues**
>
> - The exercises on this page focus on distinguishing the responsibilities of the two layers, the content of feedback, and the order of the loop. For Question 1, choose "the mutation strategy and key parameter configuration of the evolutionary algorithm itself"; you can prompt: what is being evolved is not the solution but the configuration. For Question 2, choose the inner layer; the inner layer runs differential evolution to solve the task under a given configuration. For Question 3, choose task performance, such as fitness and other performance metrics
> - What the outer layer needs is evidence of "how effective this configuration is." For Question 4, choose "the inner layer searches for solutions in the solution space, and the outer layer searches for algorithm configurations in the configuration space," and add that the two are coupled through configuration-performance. For Question 5, choose "configuration generation → task solving → performance feedback → configuration update," and memorize by causal chain: first configure, then solve, then feed back, and finally update. If students answer incorrectly, guide them back to the hierarchical responsibilities or loop arrows for re-judgment. Adaptively configure differential evolution for an engineering task. Outer-layer individuals usually encode: mutation strategy type (such as DE/rand/1/bin, DE/best/1/bin, etc.) and key parameters F, CR, NP, which can be encoded using a mixture of real-valued vectors and discrete options. The outer-layer evaluation basis comes from the inner layer: use that configuration to run differential evolution completely once on the target structural optimization task, and take metrics such as convergence accuracy, number of function evaluations required, or runtime
> - Same task, same evaluation budget, same initial population and random seed set
> - The control group uses the classic DE configuration with empirical parameters, and the experimental group uses the MetaDE evolved configuration
> - Repeat the two groups independently multiple times, compare final solution quality and convergence curves, and perform statistical tests. Cost and risk must be considered: each outer-layer evaluation of a configuration requires a complete inner-layer run, and the budget increases exponentially
> - Configurations may overfit the current task
> - Inner-layer performance noise may mislead outer-layer selection.

**Comprehension Check**

What is the inner layer searching for, and what is the outer layer searching for? What is the order of the four steps in the MetaDE loop? Being able to say "the inner layer searches solutions, the outer layer searches configurations, and performance feedback connects the two layers" can be regarded as mastery.

> **Transition Tip**
>
> After confirming that students can distinguish configuration space from solution space and can restate the four-step loop, proceed to the next page's application, extension, or summary.

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

### Defining the Core Elements for Connecting to EvoX

*Page 12*

> **Page Objectives:** Learners should master the key questions and steps that need to be clarified when connecting real tasks to EvoX.

> **Page Objectives**
>
> Understand that when connecting to EvoX, a real task should first be formalized as an evolvable exploration system, and only then should operators and parallelism be discussed. The core is to answer three questions: what varies, what is measured, and what is retained.

**Key Teaching Points**

- 1. Three-element framework. Mutable objects (exploration space) clarify what objects are allowed to change and their legal variation ranges and hard constraints, and can cover parameters, structures, and even program-level objects
- Too narrow a boundary compresses diversity, while too broad a boundary makes the evaluation chain unexecutable. Observable results (evaluation signals) should distinguish objective metrics, constraint metrics, and necessary surrogate signals, and evaluate noise, single evaluation cost, and repeated evaluation consistency
- Surrogate signals can only be used for retention judgment after calibration with the true objective. Retention criteria (selection mechanism) define the criteria by which changes are retained, such as fitness thresholds, multi-objective dominance relations, constraint satisfaction, or preference mechanisms; it determines whether exploration is guided or wasted. 2. Closed loop. Explorable (variation space) × executable (evaluation chain) × verifiable (retention criteria) are all indispensable. Without a variation space there is no search, without an evaluation chain there is no feedback, and without retention criteria there is no accumulation
- If any link is missing, evolution degenerates into an idling loop. 3. Common misunderstandings and engineering implications. Do not first choose operators and set the degree of parallelism, then go back to fill in element definitions
- Reversing the order will cause population initialization, operator design, and multi-objective evaluation to lack a basis for convergence. The clearer the element definitions, the more they can be mapped to tensorized computation graphs, Runtime scheduling, and heterogeneous computing power management: mutable objects correspond to tensor dimensions and value domains, observable results correspond to batch evaluation chains, and retention criteria correspond to the scheduling logic of screening and inheritance in each generation. 4. Mnemonic and diagnosis. When connecting a new task, answer in the order of "what varies → what is measured → what is retained." If candidate solutions often cannot be evaluated and the evaluation chain frequently reports errors, it is likely that the boundary is too broad
- If the population quickly homogenizes and hardly changes after a few generations, it is likely that the boundary is too narrow. Label each observation result with noise level, single evaluation cost, and repeated evaluation consistency.

**Exercise Handling**

This page has 5 multiple-choice questions. The correct option for Question 1 is "first clarify the three elements of mutable objects, observable results, and retention criteria"; the correct option for Question 2 is "the evaluation chain becomes unexecutable, and search loses verifiability"; the correct option for Question 3 is "distinguish objective metrics, constraint metrics, and necessary surrogate signals, and evaluate their noise, cost, and repeatability"; the correct option for Question 4 is "determine whether candidate changes enter the next generation, that is, whether exploration is guided or wasted"; the correct option for Question 5 is "determine whether it can be efficiently mapped to tensorized computation graphs, Runtime scheduling, and heterogeneous computing power management." In feedback, emphasize: operators and parallelism are implementation details, not the starting point;
the typical symptom of too narrow a boundary is decreased diversity and premature convergence, and the typical symptom of too broad a boundary is a dragged-down evaluation chain.

Campus unmanned delivery connects to EvoX. Mutable objects may include pickup and delivery point visit order and timing, route vehicle allocation, speed curve parameters, and when necessary the scheduling rule structure; hard constraints include at least load upper limits and campus speed limits (including restricted zones and time windows). Observable results can be layered into objective metrics such as total delivery cost, average delivery time, or overtime rate; constraint metrics such as load violation amount, speed limit and restricted area violation counts, and time window satisfaction rate; and surrogate signals such as congestion level or number of path conflicts. The retention criterion is recommended to use a combination of "constraint satisfaction first + multi-objective dominance relations," first screening feasible solutions, then retaining non-dominated solutions in cost and timeliness.

If the verifiable link is missing, exploration loses effective feedback, the population cannot accumulate advantageous structures, and evolution degenerates into continuous idling.

**Comprehension Check**

Ask students to restate the three elements and their closed-loop relationship in their own words, and judge the symptoms of too narrow or too broad a boundary in an example.

> **Transition Tip**
>
> After confirming that students can state how "mutable objects—observable results—retention criteria" form a closed loop, proceed to the next page's examples, applications, or summary.

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

### The Complete Workflow of the EvoX Runtime

*Page 13*

> **Page Objectives:** Learners should understand the collaborative closed loop of models, tools, environment, and evolutionary mechanisms in the EvoX runtime.

> **Page Objectives**
>
> Understand that the EvoX runtime is not a one-time inference call, but organizes models, tools, environment, and evolutionary mechanisms into a closed-loop execution system, allowing candidates to continuously evolve after execution, feedback, selection, acceptance, and inheritance.

> **Lecture Sequence**
>
> First give the overall loop chain, then explain the six links one by one; then emphasize two mechanisms that are easily underestimated—project-level persistence of cross-agent context and strict acceptance and historical inheritance; finally use exercises and cases to check whether students truly master the order of links and responsibility boundaries.

**Key Teaching Points**

- Candidate proposal → Runtime scheduling and tool execution → environmental feedback → evolutionary selection → acceptance and persistence → historical inheritance, then back to the model/agent. This order has dependencies: without execution there is no environmental feedback, without feedback there can be no effective selection, and acceptance and persistence must occur after selection. The six links of the loop: Based on problem definition, historical state, and constraints, generate parameter-level, operator-level, strategy-level, and even program-level candidates, determining from which entry points the search space is entered. Runtime invokes evaluators, simulations or surrogate models, operator libraries, and workflows, transforming candidates into actual computation and observable environmental changes. The task environment gives metrics, constraint violations, failure modes, and execution trajectories; these signals are used both for scoring and for guiding the next round of proposals. Through population updating, mutation/crossover, Pareto or preference selection, retain high-value candidates and transform or eliminate low-value candidates. Responsible for module orchestration, state management, scheduling, heterogeneous computing power allocation, and reproducible execution, decoupling algorithms from problems and combining them as needed. Cross-agent context preserves project-level state, and strict acceptance determines which results are precipitated as trusted baselines. Two mechanisms that are easily underestimated: Single-session context disappears when the dialogue ends, while evolution requires cross-session accumulation. EvoX writes candidates, evaluation records, and conclusions into project-level storage, and new agent sessions can directly load history without repeating completed work. Not all results are worth entering the baseline. The acceptance gateway checks metrics, constraints, and reproducibility, and only passing versions are written into history and inherited by subsequent rounds. This makes inheritance a trusted accelerator rather than an amplifier of errors. The next round of exploration does not need to start from zero, but continues evolving on tested results. The model is responsible for proposing possibilities, and the system is responsible for verification, selection, and accumulation; this is the continuous operating foundation of AI Native research. Key reminder: distinguish "one generation" from "one round of evolution"—the former only generates candidates, while the latter also includes execution, feedback, selection, acceptance, and inheritance
- If any link is missing, the system cannot continuously improve.

**Exercise Handling**

Question 1 checks the order of the six steps; the correct option is "candidate proposal → Runtime scheduling and tool execution → environmental feedback → evolutionary selection → acceptance and persistence → historical inheritance, then back to the model/agent"; incorrect options usually reverse the order or let selection occur before feedback. Question 2 checks Runtime responsibilities: Runtime is responsible for module orchestration, state management, scheduling, heterogeneous computing power allocation, and reproducible execution; it does not replace evaluators, is not responsible for proposing candidates, and does not make selections for the evolutionary mechanism. Question 3 checks the handling of constraint violations: violations should be passed as feedback signals to the evolution and acceptance links, may be eliminated, or may be repaired and enter the next round;
it saves candidates, evaluation records, and conclusions as project-level state, allowing new sessions to load historical baselines; it is not merely saving dialogue context, nor is it equivalent to saving model weights, and it is certainly not maintained by the model itself during dialogue. Question 5 checks the relationship between acceptance and inheritance: only results that pass metric, constraint, and reproducibility checks are suitable as trusted baselines to be inherited by subsequent rounds; without acceptance, inheritance will solidify accidental, non-reproducible conclusions and become an amplifier of errors.

In the scenario, the agent generates 40 groups of candidates, the Runtime schedules the simulation evaluator and surrogate model in batches, returns multi-objective metrics and constraint violations, the evolutionary mechanism retains 8 groups according to Pareto, and the acceptance gateway writes 1 group into project history after completing reproducibility checks; the next session loads that history and continues mutation optimization. Students can be guided to identify: environmental feedback includes multi-objective metrics, constraint violation records, evaluation trajectories, and reproducibility information; they respectively affect evolutionary selection, acceptance judgment, and the next round of proposals.

If acceptance is canceled and all 8 groups are written into history, the risk is that the baseline is contaminated, errors accumulate and amplify over time, subsequent mutations unfold around the wrong starting point, search cost rises while benefit declines, history is diluted by low-credibility entries, and it becomes difficult to judge truly usable verification conclusions.

**Comprehension Check**

Ask students to restate the six-step order in their own words, and explain the responsibility boundaries of "the model proposes candidates, the Runtime organizes execution, the evolutionary mechanism makes selections, the acceptance gateway checks, and the persistence system inherits." If students describe the Runtime as the decision-maker or candidate proposer, return to the diagram to clarify.

> **Transition Tip**
>
> After confirming that students can distinguish one generation from one round of evolution and can state that acceptance is the gate for inheritance, proceed to the next page's examples, applications, or summary.

**Material Basis**

Project-level persistence of cross-agent context; strict acceptance and historical inheritance mechanisms.

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

### Classroom Practical Analysis: Evolutionary Multi-Path vs. Preset Single-Path

*Page 14*

> **Page Objectives:** Learners should analyze the performance advantages of evolutionary multi-path in the face of unknown disturbances through engineering examples.

> **Page Objectives**
>
> Through an engineering optimization example with unknown disturbances, compare the performance of a preset single path and evolutionary multi-path. The point is not to prove that evolution is always better, but to understand how multi-path incorporates uncertainty, diversity, and online feedback into the search process, thereby improving robustness and adaptability.

> **Lecture Sequence**
>
> Choose an engineering optimization problem into which unknown disturbances can be injected, such as dynamic parameter optimization or a search task with noisy constraints; fix the objective function, evaluation budget, and disturbance sequence to ensure the two experimental groups are comparable.
>
> Fix the algorithm, hyperparameters, and search direction, and solve once according to offline assumptions. Performance may be good before disturbance, but lacking online adjustment, it is prone to falling into local optima or sharply degrading after disturbance.
>
> Use populations, multiple strategies, or multiple candidates for parallel search, and continuously adjust through mutation, crossover, selection, and environmental feedback. Multi-path is not simply repeated running, but uses online information to form exploration redundancy.
>
> The horizontal axis is disturbance intensity or evaluation round, and the vertical axis is performance retention rate. After the disturbance injection point, the preset single path drops sharply and remains low; the evolutionary multi-path shows a recovery window and maintains a certain range of candidate diversity. The diagram can directly compare performance decline magnitude, recovery window, and candidate diversity.
>
> Disturbances may include objective shifts, constraint changes, increased noise, environmental state switching, or unmodeled variables, and especially observe disturbances not seen in the preset stage. Comparison metrics include pre-disturbance optimal value, post-disturbance performance decline magnitude, recovery speed, success rate, candidate diversity, and the computation and evaluation cost of achieving equivalent performance.
>
> The fragility of a single path comes from fixed assumptions and insufficient diversity; the advantage of multi-path comes from continuous exploration, fitness feedback, and selection pressure, but computation cost, convergence stability, and engineering interpretability must be traded off. Conclusions should be based on fair budgets, reproducible experiments, and clear disturbance boundaries, avoiding misreading multi-path as unconditionally superior to single-path.

**Key Teaching Points**

- The two experimental groups must use the same objective function, evaluation budget, and disturbance sequence, and repeat multiple times and average; otherwise conclusions may be misled by initial conditions or random fluctuations. Do not test only within the training distribution; inject disturbances unseen in the preset stage such as objective shifts, constraint changes, increased noise, or environmental switching to test true robustness. Exploration redundancy brings adaptability, but also increases computation and evaluation costs, and may affect convergence stability and interpretability, requiring trade-offs under specific engineering constraints. The key to judging single-path failure is not disturbance intensity or computing resources themselves, but fixed offline assumptions, lack of online adjustment, and insufficient diversity.

**Exercise Handling**

What most needs to be kept consistent between the two groups is the "evaluation budget and disturbance sequence." Algorithm type, population size, and mutation operator belong to the strategy differences being compared, not experimental conditions that should be fixed. Feedback for correct answers: able to connect to the fair comparison principle on this page and explain the basis; prompt for incorrect answers: return to the explanation on this page and find the difference between control variables and comparison factors.

The most likely reason the preset single path sharply degrades after disturbance is "fixed offline assumptions, lack of online adjustment and diversity." Feedback for correct answers: able to grasp the core of missing adaptive mechanisms; prompt for incorrect answers: do not attribute the cause to external computing resources or disturbance intensity.

"Multi-path" in evolutionary multi-path essentially means "using populations, multiple strategies, or multiple candidates in parallel and using online feedback to form exploration redundancy." It is not simply repeated running, nor merely adding resources or complex parameter tuning.

The metric that best reflects the ability to recover after adapting to disturbance is "recovery speed and success rate." The pre-disturbance optimal value and diversity are auxiliary metrics, and decline magnitude mainly indicates fragility.

The most rigorous statement is "under fair budgets, reproducible experiments, and clear disturbance boundaries, multi-path usually shows stronger robustness, but additional costs must be traded off." Avoid absoluteness; it cannot guarantee global optimality, and single-path still has value in stable environments.

When designing fair and reproducible experiments, fix the objective function, evaluation budget, and disturbance sequence; the two groups use the same initial conditions and random seeds; repeat experiments multiple times and average; record pre-disturbance optimal value, post-disturbance performance decline magnitude, recovery speed, success rate, candidate diversity, and computation cost; clarify disturbance boundaries and injection timing.

Single-path relies on offline assumptions and has insufficient diversity; after disturbance it cannot adjust online and is prone to local optima; multi-path uses population or multi-strategy parallel search and continuously adjusts through mutation, crossover, selection, and environmental feedback, forming exploration redundancy and thus enabling recovery. The additional cost is mainly computation and evaluation overhead; if the task has high requirements for robustness and adaptability, and the recovery benefit exceeds the cost, it is worth adopting; otherwise, it is necessary to trade off among convergence stability, interpretability, and engineering constraints.

**Comprehension Check**

Randomly ask one student to summarize the key points of this page in their own words, and the instructor supplements missing keywords based on the answer.

Why must fair comparison fix the evaluation budget and disturbance sequence? If only the algorithm type is changed, can it show that multi-path is better?

Under what conditions does the advantage of multi-path hold? What additional costs must be paid?

> **Transition Tip**
>
> After confirming that students can state the three relationships of "fair comparison is the premise of conclusions," "disturbances should cover unseen changes," and "multi-path is not unconditionally better," proceed to the next page's examples, applications, or summary.

**Keyword Coverage**

- Preset single path, evolutionary multi-path, unknown disturbance, online feedback, exploration redundancy, robustness, adaptability, fair comparison, evaluation budget, disturbance sequence, performance retention rate, recovery window, candidate diversity, computation cost, convergence stability, interpretability.

> **Material Clues**
>
> - The performance comparison line chart shows the disturbance injection point, the sharp decline and sustained low performance of the single path, the recovery window of multi-path, and the range of candidate diversity, and can serve as the core visual basis for classroom analysis.

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

### Review of the Course's Core Ideas and Evolutionary Origins

*Page 15*

> **Page Objectives:** Learners should systematically review the core logic of the biological inspiration and historical origins of evolutionary computation.

> **Page Objectives**
>
> Intelligence is not a unique product preset once and for all, but a process of continuous evolution in environmental feedback. Learners should be able to explain along the historical origins how symbolism, connectionism, cybernetics, and evolutionary computation moved from competition to complementarity, and understand why evolutionary computation takes population, mutation, selection, and fitness as its skeleton, rather than promising to write out the unique correct solution in advance.

**Key Teaching Points**

- Emphasize that the model generates possibilities, the environment provides feedback, and the system is responsible for evaluation, selection, and inheritance; the three form a loop, creating the evolutionary closed loop of "generate candidates → evaluate → select → inherit → iterate." This can link back to Turing's 1950 "child machine" idea: capability is not written once and for all, but gradually acquired through education, experimentation, and rewards and punishments. Symbolism—explicit representation and formal reasoning, suitable for problems with clear rules and closed boundaries
- Connectionism—distributed representation and learning, skilled at perception and generalization
- Cybernetics—goal regulation in feedback loops, answering how a system maintains direction amid disturbances
- Evolutionary computation—population adaptation for open search spaces, maintaining diversity and search pressure through populations. Remind students to self-check with four questions: Where are the structural constraints? Where do perception and generation come from? What does closed-loop regulation rely on? Who is responsible for exploration and diversity? The symbolic layer provides interpretable structural constraints and verification conditions, the connectionist layer provides perception and generation capabilities, the control layer ensures feedback and goal regulation, and the evolutionary layer is responsible for searching in uncertain environments and preserving diverse solutions. Complex intelligent systems need to connect capabilities into loops, rather than expecting one-time output. Bionic mechanisms such as mutation, selection, inheritance, and diversity should be abstracted into computable, parallelizable, and composable optimization processes
- Fitness is a measurable objective function, mutation is often characterized by probability distributions, and selection pressure is implemented by sampling and retention rules. Self-check three items: whether the encoding expresses the true structure of the solution
- whether fitness is measurable and consistent with the objective
- whether selection pressure is neither too strong (premature convergence) nor too weak (random drift). Real tasks often have three characteristics—multi-objective conflict, sparse feedback, and uncertain environments. When objectives conflict, there is usually no single-point solution that is simultaneously optimal, only a set of trade-off solutions
- When feedback is sparse and delayed, a complete evaluation cannot be obtained at once
- Environmental changes make a static unique solution quickly obsolete. Evolutionary computation generates many candidates in parallel, evaluates their trade-off performance, preserves diverse solutions, and postpones "which one to choose" to a stage with more sufficient information or clearer preferences. When the search space is huge, serial loops struggle to complete sufficient sampling and continuous iteration. GPUs, tensors, and a unified runtime turn population-level parallelism, operator parallelism, and long-term online iteration into an engineering foundation, increasing the scale of evaluable candidates per unit time and population diversity. But it must be emphasized: parallelism only increases feasible scale; it does not replace fitness design and selection pressure design. Biological metaphors in mechanisms do not equal reliable results
- Diversity is not noise, but a deliverable. If the system rapidly converges to a single form each generation, it may have lost the ability to cope with uncertainty.

**Exercise Handling**

Distinguish evolutionary computation from a preset unique solution. The correct option is continuous exploration of candidate solutions through population-level "generation—evaluation—selection—iteration." In feedback, grasp four keywords: population, mutation, selection, fitness.

The engineering value of population diversity. The correct option is to preserve trade-off space for subsequent decisions and reduce the risk of premature convergence to local optima. Emphasize that in multi-objective and uncertain environments, the set of diverse solutions itself is the output.

The convergence point of the four routes. The correct option is that cybernetics focuses on feedback and goal regulation and is the prototype of closed-loop regulation. If students confuse them, return to the comparison table: symbolism equals explicit representation, connectionism equals distributed learning, cybernetics equals feedback regulation, and evolutionary computation equals population adaptation.

The bottleneck of serial loops. The correct option is insufficient sampling, making it difficult to cover enough candidates and maintain continuous iteration. The key is not whether it can compute, but whether it computes enough and long enough.

Engineering abstraction. The correct option is to implement mechanisms such as mutation, selection, and populations as computable, parallelizable, and composable operators, and use encoding and fitness matching the task to test their effectiveness. Remind that bionics is only the starting point; reproducible operators and measurable evaluation are the criteria for implementation.

Writing out a unique optimal plan in advance fails because energy consumption, cost, and reliability conflict with each other, feedback is sparse and delayed, and seasonal parameters continuously change; evolutionary computation uses the cycle of "generate candidates—evaluate—select—inherit—iterate" to preserve a set of diverse layouts and postpone the final choice until information is more sufficient. Question two: GPUs and a unified runtime can change batch fitness evaluation to population-level parallelism and operator parallelism, making long-term online iteration and maintenance of large-scale populations feasible; parallelism is an engineering foundation that determines the number of evaluable candidates per unit time and population diversity; but it cannot replace the matching design of encoding, fitness, and selection pressure.

**Comprehension Check**

Ask students to explain in one sentence what links the "evolutionary loop" includes, and give an example of the division of labor among the four routes. If students only remember the bionic metaphor, ask further whether encoding, fitness, and selection pressure match the task.

> **Transition Tip**
>
> After confirming that students can distinguish the four routes and explain the relationship among diversity, parallel exploration, and the evolutionary loop, proceed to the next page's application, case, or summary.

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

### Summary and Outlook: Opening a New Practice of AI Native Research

*Page 16*

> **Page Objectives:** Learners should clarify the guiding significance of this lesson's topic for subsequent AI Native research practice.

> **Page Objectives**
>
> As the conclusion and outlook of this lesson, help learners converge the ideological sources of evolutionary computation, the context of intelligence history, and the capability boundaries of large models onto EvoX's unified evolutionary architecture, and establish a basis for judging whether a research plan truly has AI Native characteristics.

> **Lecture Sequence**
>
> Mutation provides diversity, selection retains effective individuals, inheritance transmits across generations, and environmental adaptation determines the direction of trade-offs; this is an adaptive solution paradigm that does not preset an optimal individual. Second, the historical context of intelligence: in 1950 Turing proposed the "child machine," advocating gradually acquiring abilities in the environment through education, experimentation, and rewards and punishments; culture and computing power further compressed the evolutionary cycle from generations to years, months, and even a single iteration. Third, technical necessity: large models have expanded the boundaries of generation, representation, and reasoning, but single-pass generation cannot self-verify correctness and is also difficult to remain effective across environments; real research still requires large-scale parallel exploration, evaluation, and a feedback loop.

**Key Teaching Points**

- The core conclusion of this page is not a simple superposition of "large models plus evolutionary algorithms," but the re-convergence of three main lines. EvoX's unified evolutionary architecture can be summarized as: the model generates possibilities, the environment provides feedback, and the system is responsible for evaluation and inheritance. Therefore, the key shift in AI Native research is to organize "problem exploration → candidate generation → parallel evaluation → continuous improvement" into a reusable process, rather than treating AI as a one-time single-point tool. The old paradigm is "preset answers/single-point algorithms," and the entry point of the new practice is "environmental process/continuous evolution."

**Keyword Coverage**

- Mutation, selection, inheritance, environmental adaptation, Turing's "child machine," large model capability boundaries, large-scale parallel exploration, environmental feedback, evaluation and inheritance, EvoX, problem exploration, candidate generation, parallel evaluation, continuous improvement, AI Native research.

> **Material Clues**
>
> - The page's vision blueprint presents from left to right "biological evolutionary origins" and "intelligence history context" jointly inspiring the capability boundaries of large models and the parallel exploration foundation, then converging into EvoX's unified evolutionary architecture, with the final landing point being new practices in AI Native research. During explanation, students can be asked to retell this convergence relationship along the arrows.

**Exercise Handling**

Five multiple-choice questions can be used for quick checking. The correct direction for Question 1 is "mutation, selection, inheritance, and environmental adaptation," emphasizing the source of biological inspiration; the correct direction for Question 2 is "single-pass generation cannot self-verify correctness in a real environment and is also difficult to remain effective amid changes in environment and models"; the correct direction for Question 3 is "gradually acquiring abilities in the environment through education, experimentation, and rewards and punishments," corresponding to the embryonic idea of feedback and evaluation inheritance; the correct direction for Question 4 is that the system is responsible for "evaluation, selection, error correction, and inheritance"; the correct direction for Question 5 is to organize exploration, generation, evaluation, and improvement into a reusable process. If students answer incorrectly, ask them to return to the division of labor among "model—environment—system" for repositioning.

The perovskite formulation case is suitable as a comprehensive application. The diagnostic point is that the team lacks standardized evaluation, environmental feedback return, and selection and inheritance mechanisms; "one-time generation + manual selection" cannot support an iterative process that is parallelizable, evaluable, and accumulative. The improvement plan can revolve around a three-stage loop: the candidate generator is responsible for generating under constraints and controlled mutation; the parallel evaluation environment is responsible for unifying measurement protocols, recording batches and process parameters, and improving evaluation throughput; the selection and inheritance module retains elite solutions according to comprehensive fitness and precipitates failure samples and attribution conclusions. Students can be prompted to assign one evaluation metric to each component, such as candidate diversity, effective candidate rate, evaluation throughput, inter-batch repeatability, optimal fitness improvement per round, and knowledge reuse rate.

**Comprehension Check**

Ask students to say without notes "problem exploration → candidate generation → parallel evaluation → continuous improvement," and point out which of the model, environment, and system leads each step. Being able to explain the division of labor clearly indicates that they have grasped the key points of AI Native research. You can also ask further: "If a process still relies on single-pass generation plus manual comparison, has it already entered the evolutionary paradigm?" Guide students to judge by "whether it forms a sustainably operating closed loop."

**Classroom Activity**

Use "four things of evolution" as a checklist, and ask students, when facing a system claiming to be "evolutionary," to check item by item: How does it generate diversity? By what standard does it screen? How are excellent solutions retained and transmitted? From which environmental feedback is fitness obtained? Only when all four are present does it constitute a closed loop.

> **Transition Tip**
>
> After confirming that students can state the core relationships on this page, especially the division of labor among model, environment, and system, and the significance of moving from single-point algorithms to environmental processes, proceed to subsequent in-depth study of the EvoX intelligent evolution system.