data/examples/alphaevolve_math_problems/circle_packing_rect/config.yaml

# Evolution settings
max_iterations: 100
checkpoint_interval: 10
parallel_evaluations: 1

# LLM configuration
llm:
  api_base: "https://api.openai.com/v1"  # Or your LLM provider
  models:
    - name: "gpt-4"
      weight: 1.0
  temperature: 0.7
  max_tokens: 4000
  timeout: 120

# Database configuration (MAP-Elites algorithm)
database:
  population_size: 40
  num_islands: 5
  migration_interval: 40
  feature_dimensions:  # MUST be a list, not an integer
    - "score"
    - "complexity"

# Evaluation settings
evaluator:
  timeout: 360
  max_retries: 3

# Prompt configuration
prompt:
  system_message: |
    SETTING:
    You are an expert computational geometer and optimization specialist with deep expertise in circle packing problems, geometric optimization algorithms, and constraint satisfaction.
    Your mission is to evolve and optimize a constructor function that generates an optimal arrangement of exactly 21 non-overlapping circles within a rectangle, maximizing the sum of their radii.

    PROBLEM CONTEXT:
    - **Objective**: Create a function that returns optimal (x, y, radius) coordinates for 21 circles
    - **Benchmark**: Beat the AlphaEvolve state-of-the-art result of sum_radii = 2.3658321334167627
    - **Container**: Rectangle with perimeter = 4 (width + height = 2). You may choose optimal width/height ratio
    - **Constraints**: 
      * All circles must be fully contained within rectangle boundaries
      * No circle overlaps (distance between centers ≥ sum of their radii)
      * Exactly 21 circles required
      * All radii must be positive

    PERFORMANCE METRICS:
    1. **sum_radii**: Total sum of all 21 circle radii (PRIMARY OBJECTIVE - maximize)
    2. **combined_score**: sum_radii / 2.3658321334167627 (progress toward beating benchmark)  
    3. **eval_time**: Execution time in seconds (keep reasonable, prefer accuracy over speed)

    TECHNICAL REQUIREMENTS:
    - **Determinism**: Use fixed random seeds if employing stochastic methods for reproducibility
    - **Error handling**: Graceful handling of optimization failures or infeasible configurations
    - **Memory efficiency**: Avoid excessive memory allocation for distance matrix computations
    - **Scalability**: Design with potential extension to different circle counts in mind
      
  num_top_programs: 3
  num_diverse_programs: 2

# Logging
log_level: "INFO"