Add AlphaEvolve integration plan for issue #15

AlphaEvolve.md

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+# Integrating AlphaEvolve's Approaches into Darwin Gödel Machine (DGM)
+
+## Table of Contents
+1. [Introduction](#introduction)
+2. [Background](#background)
+   - [Darwin Gödel Machine (DGM)](#darwin-gödel-machine-dgm)
+   - [AlphaEvolve](#alphaevolve)
+   - [Comparative Analysis](#comparative-analysis)
+3. [Proposed Integration](#proposed-integration)
+   - [Architectural Extensions](#architectural-extensions)
+   - [Dual Evolution Framework](#dual-evolution-framework)
+   - [Integration Points](#integration-points)
+4. [Implementation Plan](#implementation-plan)
+   - [Phase 1: Foundation](#phase-1-foundation)
+   - [Phase 2: Program Evolution](#phase-2-program-evolution)
+   - [Phase 3: Integration](#phase-3-integration)
+   - [Phase 4: Evaluation](#phase-4-evaluation)
+5. [Technical Considerations](#technical-considerations)
+   - [Performance Optimizations](#performance-optimizations)
+   - [Safety Mechanisms](#safety-mechanisms)
+   - [Compatibility Concerns](#compatibility-concerns)
+6. [Evaluation Framework](#evaluation-framework)
+   - [Metrics](#metrics)
+   - [Benchmarks](#benchmarks)
+   - [Success Criteria](#success-criteria)
+7. [Timeline and Milestones](#timeline-and-milestones)
+8. [References](#references)
+
+## Introduction
+
+This document outlines a plan for integrating concepts from Google's AlphaEvolve into the Darwin Gödel Machine (DGM) framework. While DGM focuses on evolving the coding agent itself, AlphaEvolve employs evolutionary search directly over functions and programs. The integration aims to create a hybrid approach that can leverage the strengths of both methodologies to enhance the system's capabilities for self-improvement and algorithm discovery.
+
+## Background
+
+### Darwin Gödel Machine (DGM)
+
+DGM is a novel self-improving system that iteratively modifies its own code and empirically validates each change using coding benchmarks. Key characteristics include:
+
+- **Agent Evolution**: DGM evolves the coding agent itself, improving its ability to write and modify code
+- **Benchmark-Driven**: Improvements are validated against coding benchmarks (SWE-bench and Polyglot)
+- **Git-based Tracking**: Modifications are tracked as git patches
+- **Docker Isolation**: Execution occurs in isolated Docker containers for safety
+- **Generation-based Selection**: Uses various selection strategies to choose parents for the next generation
+
+### AlphaEvolve
+
+AlphaEvolve, developed by Google DeepMind, is a Gemini-powered coding agent for designing advanced algorithms. Key characteristics include:
+
+- **Program Evolution**: Directly evolves functions/programs rather than the agent
+- **Mutation Operators**: Employs a rich set of program transformation operators
+- **LLM-guided Search**: Uses LLMs to guide the evolutionary search process
+- **Algorithmic Focus**: Specifically targets algorithm optimization
+- **Performance-driven Selection**: Selects candidates based on direct performance metrics
+
+### Comparative Analysis
+
+| Aspect | DGM | AlphaEvolve |
+|--------|-----|-------------|
+| Evolution Target | Coding Agent | Programs/Functions |
+| Primary Goal | General Coding Capability | Algorithm Optimization |
+| Evaluation Mechanism | Coding Benchmarks | Algorithm Performance Metrics |
+| Modification Scope | Agent's Codebase | Individual Programs |
+| Selection Strategy | Various (score-proportional, etc.) | Performance-based |
+
+## Proposed Integration
+
+### Architectural Extensions
+
+The integration will require extending DGM's architecture to support two parallel evolution mechanisms:
+
+1. **Agent Evolution** (existing DGM approach)
+2. **Program Evolution** (AlphaEvolve approach)
+
+These mechanisms will be designed to complement each other, with the possibility of information exchange between them.
+
+### Dual Evolution Framework
+
+We propose a dual evolution framework where:
+
+1. The Agent Evolution component continues to evolve the coding agent itself
+2. A new Program Evolution component evolves specific programs and algorithms
+3. A Coordination Layer manages interactions between the two evolution processes
+
+```
+┌───────────────────────────────────────────────────────────┐
+│                   Dual Evolution Framework                │
+├───────────────────┬───────────────────┬───────────────────┤
+│  Agent Evolution  │  Coordination     │ Program Evolution │
+│      (DGM)        │      Layer        │   (AlphaEvolve)   │
+├───────────────────┼───────────────────┼───────────────────┤
+│                    Shared Resources                        │
+│         (Benchmarks, Models, Selection Strategies)        │
+└───────────────────────────────────────────────────────────┘
+```
+
+### Integration Points
+
+1. **Shared Evaluation Framework**: Extend the current evaluation system to handle both agent and program performance
+2. **Cross-pollination Mechanisms**: Allow successful patterns from one evolution stream to inform the other
+3. **Unified Selection**: Develop selection strategies that can operate across both evolution types
+4. **Resource Allocation**: Implement mechanisms to balance computational resources between the two approaches
+
+## Implementation Plan
+
+### Phase 1: Foundation
+
+1. **Create Program Evolution Module**
+   - Implement the basic structure for program evolution
+   - Define mutation operators for program transformation
+   - Establish program representation format
+
+2. **Extend Evaluation Framework**
+   - Add support for evaluating individual programs/functions
+   - Integrate algorithm-specific performance metrics
+   - Maintain backward compatibility with existing DGM evaluation
+
+3. **Implement Coordination Layer**
+   - Design interfaces between agent and program evolution
+   - Create mechanisms for sharing improvements
+   - Implement resource allocation strategies
+
+### Phase 2: Program Evolution
+
+1. **Implement Mutation Operators**
+   - Function transformation operators
+   - Program structure modifications
+   - Parameter optimization
+
+2. **Create LLM-guided Evolution**
+   - Integrate LLM-based mutation suggestions
+   - Implement heuristics for promising search directions
+   - Develop repair mechanisms for invalid mutations
+
+3. **Build Program Selection Strategies**
+   - Performance-based selection
+   - Diversity-promoting mechanisms
+   - Novelty search components
+
+### Phase 3: Integration
+
+1. **Develop Cross-pollination Mechanisms**
+   - Agent improvements based on evolved programs
+   - Program templates based on agent capabilities
+   - Shared knowledge representation
+
+2. **Create Unified Selection Framework**
+   - Multi-objective selection strategies
+   - Pareto-optimal selection across both domains
+   - Dynamic resource allocation
+
+3. **Implement Safety & Constraints**
+   - Extend Docker isolation for program evaluation
+   - Implement resource usage limitations
+   - Add additional code safety analysis
+
+### Phase 4: Evaluation
+
+1. **Benchmark Suite Extension**
+   - Add algorithm-specific benchmarks
+   - Integrate complexity and efficiency metrics
+   - Create hybrid evaluation scenarios
+
+2. **Comparative Analysis**
+   - DGM vs. AlphaEvolve vs. Integrated approach
+   - Ablation studies on integration components
+   - Long-term evolution trajectories
+
+3. **Documentation & Publication**
+   - Document the integrated system
+   - Prepare visualization of results
+   - Summarize findings for potential publication
+
+## Technical Considerations
+
+### Performance Optimizations
+
+- **Parallel Evaluation**: Implement parallel evaluation of program variants
+- **Caching Mechanisms**: Cache evaluation results for similar programs
+- **Incremental Testing**: Only re-evaluate on affected test cases after small mutations
+
+### Safety Mechanisms
+
+- **Execution Sandboxing**: Enhance the Docker isolation with additional constraints
+- **Resource Limiting**: Implement CPU, memory, and time limits for program execution
+- **Static Analysis**: Add pre-execution static analysis to catch potential issues
+
+### Compatibility Concerns
+
+- **API Compatibility**: Ensure backward compatibility with existing DGM interfaces
+- **Dataset Formats**: Design unified formats for both evolution approaches
+- **Model Support**: Handle differences in model requirements between approaches
+
+## Evaluation Framework
+
+### Metrics
+
+- **Agent Metrics**: Code quality, task completion, correctness
+- **Program Metrics**: Runtime efficiency, memory usage, correctness
+- **Combined Metrics**: Overall system improvement rate, diversity of solutions
+
+### Benchmarks
+
+- **Coding Tasks**: SWE-bench and Polyglot benchmarks (existing)
+- **Algorithm Tasks**: Sorting, pathfinding, optimization problems
+- **Novel Challenges**: Previously unseen problems to test generalization
+
+### Success Criteria
+
+- **Performance Improvement**: Better results than either approach alone
+- **Convergence Speed**: Faster discovery of high-quality solutions
+- **Solution Diversity**: Greater variety of viable approaches
+- **Generalization**: Better performance on unseen challenges
+
+## Timeline and Milestones
+
+| Week | Milestone | Deliverables |
+|------|-----------|-------------|
+| 1-2 | Foundation Setup | Program evolution module, extended evaluation framework |
+| 3-4 | Program Evolution | Mutation operators, LLM guidance implementation |
+| 5-6 | Integration | Cross-pollination mechanisms, unified selection |
+| 7-8 | Evaluation | Benchmark results, comparative analysis |
+
+## References
+
+1. Darwin Gödel Machine: Open-Ended Evolution of Self-Improving Agents - Zhang et al., 2025
+2. AlphaEvolve: A Gemini-powered Coding Agent for Designing Advanced Algorithms - Google DeepMind
+3. OpenEvolve implementation - https://github.com/codelion/openevolve