openevolve/examples/circle_packing_with_artifacts/config.yaml at 438fdc8845a83f7f8856adfd6f6a293479d61f1d · algorithmicsuperintelligence/openevolve · GitHub

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# Circle Packing Benchmark Configuration with Thompson Sampling
# Based on config_benchmark.yaml but uses two models with Thompson sampling

max_iterations: 500
checkpoint_interval: 10
log_level: "INFO"
random_seed: 42

# Full rewrite mode (best for constructor-based problems)
diff_based_evolution: false
max_code_length: 50000

# LLM Configuration - Two models
llm:
  api_base: "https://openrouter.ai/api/v1"
  models:
    - name: "google/gemini-2.5-flash-lite"
      weight: 0.8
    - name: "google/gemini-2.5-flash"
      weight: 0.2

  temperature: 0.4
  top_p: 0.95
  max_tokens: 16000
  timeout: 180
  retries: 3

# Prompt Configuration
prompt:
  system_message: |
    You are an expert mathematician specializing in circle packing problems and computational geometry.
    Your task is to improve a constructor function that places 26 circles in a unit square to maximize the sum of their radii.

    Target: AlphaEvolve achieved sum of radii = 2.635 for n=26.

    Key insights:
    - This is a constrained optimization problem with many local minima
    - Local optimization methods may get stuck - consider approaches that explore the solution space more broadly
    - Multiple starting points or perturbation strategies can help find better solutions
    - Good initial placements matter: hexagonal patterns, corner utilization, edge circles
    - The problem has 78 degrees of freedom (26 centers + 26 radii)

    Think about how to formulate this mathematically and what optimization strategies might help escape local minima.

  num_top_programs: 3
  num_diverse_programs: 2

  # Artifacts enabled for debugging/visualization data
  include_artifacts: true
  max_artifact_bytes: 20480  # 20KB

# Database Configuration
database:
  population_size: 100
  archive_size: 50
  num_islands: 4  # Optimal island count

  # Selection parameters
  elite_selection_ratio: 0.1
  exploration_ratio: 0.4   # Higher exploration to discover optimization approaches
  exploitation_ratio: 0.5  # Balance with exploitation

  # Feature dimensions for MAP-Elites (diversity-focused metrics)
  # - radius_variance: separates uniform vs varied circle sizes (0-1 normalized)
  # - spatial_spread: separates clustered vs distributed centers (0-1 normalized)
  feature_dimensions: ["radius_variance", "spatial_spread"]
  feature_bins: 10

  # Migration parameters - faster sharing of breakthroughs
  migration_interval: 10  # Share discoveries sooner
  migration_rate: 0.15    # Migrate more programs

# Evaluator Configuration
evaluator:
  timeout: 600  # Allow complex optimization programs to complete
  max_retries: 3
  cascade_evaluation: true
  cascade_thresholds: [0.5, 0.8]
  parallel_evaluations: 4
  use_llm_feedback: false
  enable_artifacts: true

# Novelty Detection - prevent duplicate evaluations
novelty:
  enabled: true
  embedding_backend: "local"
  embedding_model: "all-MiniLM-L6-v2"
  similarity_threshold: 0.95
  max_regeneration_attempts: 3
  temperature_increment: 0.15