EvoX Intelligent Evolution System and AI-Native Research Practice
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120 filesLesson 1
- HTML00_Lesson 1: Origins of Intelligent Evolution and the Background of EvoX.html
- HTML01_The Acceleration Trend of Intelligent Evolution and Cycle Compression.html
- HTML02_The Power of Current Large Models and the Limits of Single-Pass Generation.html
- HTML03_Convergence of Intelligence Paths and the Origins of Evolutionary Computation.html
- HTML04_Turing's Vision of the "Child Machine".html
- HTML05_New Sources of Candidate Generation as Compute Scales Up.html
- HTML06_Expanding Evolutionary Targets from Numerical Parameters to Software Systems.html
- HTML07_Real-World Complexity and Multi-Objective Trade-offs.html
- HTML08_Self-Improvement of Solving Methods and Algorithm Evolution.html
- HTML09_MetaDE: A Nested Architecture for Algorithms That Optimize Algorithms.html
- HTML10_EvoX's Evolutionary Closed-Loop System Architecture.html
- HTML11_Core Boundaries and Key Questions for Integrating Tasks into EvoX.html
- HTML12_Getting Started with EvoX: Environment Setup and Usage.html
- HTML13_A Researcher's Core Competencies and Aesthetic Judgment.html
- HTML14_Comparing Traditional Single-Turn Q&A with EvoX's Continuous Evolution System.html
- HTML15_A Vision for Real-World Intelligent Evolution Infrastructure.html
- MDAI999-Lesson-1_lecture_notes.md
Lesson 2
- HTML00_Lesson 2: Origins of Evolutionary Computation and the Convergence of Multiple Paths.html
- HTML01_The Re-Convergence of Multiple Paths to Intelligence.html
- HTML02_A Review of Symbolism, Connectionism, and Cybernetics.html
- HTML03_Evolutionary Computation as a Direct Technological Origin.html
- HTML04_Is Intelligence a Predefined Product or a Process of the Environment.html
- HTML05_Real-World Complexity and Multi-Objective Conflicts.html
- HTML06_Resolving the Tension Between Diversity and Engineering Cost.html
- HTML07_The Engineering Foundation Formed by GPUs and Large Models.html
- HTML08_Expanding the Boundaries of Evolutionary Targets: From Parameters to Programs.html
- HTML09_The Capability Boundaries of Large Models and System-Level Continuous Evolution.html
- HTML10_MetaDE: Using Evolutionary Algorithms to Improve Evolutionary Algorithms.html
- HTML11_Defining the Core Elements for Integrating with EvoX.html
- HTML12_EvoX's Complete Runtime Workflow.html
- HTML13_Hands-On Classroom Analysis: Multiple Evolutionary Paths vs. a Single Predetermined Path.html
- HTML14_Review of the Course's Core Ideas and the Origins of Evolution.html
- HTML15_Summary and Outlook: Launching a New Practice of AI-Native Research.html
- MDAI999-Lesson-2_lecture_notes.md
Lesson 3
- HTML00_Lesson 3: Real-World Complexity and Multi-Objective Trade-offs.html
- HTML01_Multi-Objective Conflicts and Real-World Complexity.html
- HTML02_Trade-offs and Multi-Objective Exploration in the Real Physical World.html
- HTML03_Finding Usable Answers and Thought Experiments.html
- HTML04_Problem Transformation: From Choosing Algorithms to an Exploration Process.html
- HTML05_Preference Optimization and Multiple Candidate Solution Sets.html
- HTML06_Multi-Objective Practical Context of Supercritical Airfoil Design.html
- HTML07_Continuity of Baseline Airfoils and Perturbation Samples.html
- HTML08_Applying Surrogate Models to Manage High Simulation Costs.html
- HTML09_Outputting Diverse Candidate Solutions That Balance All Metrics.html
- HTML10_Handling Objective Conflicts and Sparse Feedback in Complex Engineering.html
- HTML11_The Decision-Making Role of Domain Experts and Solution Evaluation.html
- HTML12_Classroom Activity: Designing a Multi-Objective Parameter Optimization Evaluation Plan.html
- HTML13_Classroom Activity Debrief: Validating the Soundness of Trade-off Solutions.html
- HTML14_Review of This Lesson's Core Concepts.html
- HTML15_Summary and Outlook: The Value of Research in a Real, Complex World.html
- MDAI999-Lesson-3_lecture_notes.md
Lesson 4
- HTML00_Lesson 4: The Modern Computing Foundation and Tensorized Parallel Evolution.html
- HTML01_The Modern Computing Foundation and Tensorized Parallel Evolution.html
- HTML02_Evolutionary Diversity and the Challenge of Computational Scale.html
- HTML03_The Efficiency Bottleneck of Traditional Serial CPU Loops.html
- HTML04_From Candidate Lists to Tensorized Computation Graphs.html
- HTML05_Tensorized Organization of Populations, Evaluations, and States.html
- HTML06_Operator Parallelism and Compiler Optimization.html
- HTML07_Designing an Open, Decoupled Foundation Architecture.html
- HTML08_Module Partitioning of Algorithms and Problems.html
- HTML09_Workflow and Monitoring Module Management.html
- HTML10_Runtime Scheduler and Heterogeneous Compute Management.html
- HTML11_Multi-Domain Applications Supported by a Unified Foundation.html
- HTML12_Hands-On Comparative Analysis of Computational Efficiency.html
- HTML13_Core Methods for Batch Tensor Computation.html
- HTML14_Runtime Scheduling and Algorithm Composability.html
- HTML15_The Cornerstone of High-Intensity Parallel Evolutionary Exploration.html
- MDAI999-Lesson-4_lecture_notes.md
Lesson 5
- HTML00_Lesson 5: Method Evolution: From Optimizing Solutions to Optimizing the Algorithms Themselves.html
- HTML01_Core Background and Motivation for Method Evolution.html
- HTML02_Technological Evolution and Historical Origins of Intelligent Systems.html
- HTML03_How Large Models and Compute Expand the Boundaries of Evolution.html
- HTML04_Multi-Dimensional Conflicts and Trade-offs in Real-World Problems.html
- HTML05_The Core Ideas and Breakthroughs of Method Evolution.html
- HTML06_Inner and Outer Dual-Layer Evolution and Adaptive Mechanisms.html
- HTML07_A Representative Method Evolution Implementation: The MetaDE Architecture.html
- HTML08_Algorithm Structure Evolution: AutoPSO and EvoGO.html
- HTML09_GPU Parallelism and Using Evolution to Improve Evolution.html
- HTML10_Designing a Blueprint for Dual-Layer Optimization Experiments.html
- HTML11_The Continuous Development of Intelligent Systems and Environmental Challenges.html
- HTML12_Getting Started and Simplified Environment Setup.html
- HTML13_A Researcher's Core Competencies and Mindset Shift.html
- HTML14_Building Sustainable Evolutionary Infrastructure.html
- HTML15_Summary and Outlook: Toward Higher-Order Forms of Intelligence.html
- MDAI999-Lesson-5_lecture_notes.md
Lesson 6
- HTML00_Lesson 6: System-Level Continuous Evolution: Genesis and the Accumulation of the Software World.html
- HTML01_The Capability Boundaries of Large Models and the Necessity of System-Level Evolution.html
- HTML02_Expanding Evolutionary Boundaries from Numerical Parameters to Program Systems.html
- HTML03_Complex Real-World Challenges: Multi-Objective Trade-offs and Diversity Preservation.html
- HTML04_Tensorized Computation and Parallel Exploration Enabled by a Unified Architecture.html
- HTML05_The Evolution of Algorithms Themselves: From Solving Answers to Improving Methods.html
- HTML06_MetaDE and AutoPSO: The Dual-Layer Architecture of Method Evolution.html
- HTML07_A Multi-Objective Practical Case Study in Supercritical Airfoil Design.html
- HTML08_Challenges Across the Lifecycle: Context Exhaustion and Project-Level Persistence.html
- HTML09_The Genesis Architecture and the Accumulation Paradigm of the Software World.html
- HTML10_Engineering Patterns for Multi-Agent Collaboration and Recursive Task Decomposition.html
- HTML11_Strict Acceptance and Historical Inheritance Mechanisms.html
- HTML12_Classroom Activity: Simulating Multi-Agent Collaboration and an Automated Acceptance Gateway.html
- HTML13_AI-Native Ways of Working and Convenient Entry Points.html
- HTML14_A Researcher's Core Taste: Human-AI Division of Labor and Critical Thinking.html
- HTML15_Course Summary: Building Infrastructure for a Continuously Evolving Software World.html
- MDAI999-Lesson-6_lecture_notes.md
Lesson 7
- HTML00_Lesson 7: AI-Native Research Paradigm and a Practical Entry into EvoX.html
- HTML01_AI-Native Research Paradigm and a Practical Entry into EvoX.html
- HTML02_The Capability Boundaries of Large Models and Continuous Evolution Systems.html
- HTML03_Transforming Real Problems into an EvoX Exploration Process.html
- HTML04_Multi-Objective Optimization and Multi-Solution Exploration.html
- HTML05_MetaDE: Using Evolutionary Algorithms to Improve Evolutionary Algorithms.html
- HTML06_AI-Native Ways of Working and Convenient Entry Points.html
- HTML07_Hands-On: Warehouse Robot Pathfinding Task.html
- HTML08_Experimental Results and Visualization Analysis.html
- HTML09_The Easier the Tools, the Clearer Human Responsibility.html
- HTML10_The Core of Research: The Ability to Ask Key Questions.html
- HTML11_Human-AI Division of Labor: AI Delivers Answers, Humans Set the Direction.html
- HTML12_Core Principle: Code is cheap. Taste matters.html
- HTML13_Classroom Activity: Guiding AI Through Conversation to Complete an EvoX Task.html
- HTML14_Course Summary: AI-Native Workflows and Scientific Taste.html
- HTML15_Appendix and Open-Source Community Entry Points.html
- MDAI999-Lesson-7_lecture_notes.md