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# K-Module Problem Configuration for OpenEvolve
# Demonstrates evolutionary search vs iterative refinement
#
# This configuration uses the same model (gemini-2.5-flash) as the
# iterative agent for fair comparison.
max_iterations: 50
checkpoint_interval: 10
log_level: "INFO"
random_seed: 123
# Full rewrite mode - the problem is about finding the right configuration
diff_based_evolution: false
max_code_length: 10000
# LLM Configuration - using lightweight model for cost efficiency
llm:
api_base: "https://openrouter.ai/api/v1"
models:
- name: "google/gemini-2.5-flash-lite"
weight: 1.0
temperature: 0.9 # Higher temperature for more exploration
top_p: 0.98
max_tokens: 4096
timeout: 60
retries: 3
# Prompt Configuration
prompt:
system_message: |
You are optimizing a data processing pipeline configuration through EXPLORATION.
The pipeline has 4 independent modules, each with 5 possible options:
- loader: ['csv_reader', 'json_reader', 'xml_reader', 'parquet_reader', 'sql_reader']
- preprocess: ['normalize', 'standardize', 'minmax', 'scale', 'none']
- algorithm: ['quicksort', 'mergesort', 'heapsort', 'bubblesort', 'insertion']
- formatter: ['json', 'xml', 'csv', 'yaml', 'protobuf']
CRITICAL: The score tells you how many modules are correct (0-4), but NOT which ones.
This means when you have 3/4 correct, ANY of the 4 modules could be wrong!
STRATEGY FOR SUCCESS:
1. When stuck at a score, try DIFFERENT options for EACH module systematically
2. Don't assume any module is definitely correct - even ones that seem obvious
3. Combine successful elements from different high-scoring configurations
4. If multiple configs have the same score, they may have DIFFERENT correct modules
Your goal: Find the configuration with 4/4 modules correct.
num_top_programs: 5 # More examples to learn from
num_diverse_programs: 3 # More diversity in examples
include_artifacts: true
max_artifact_bytes: 10240
# Database Configuration - KEY FOR EVOLUTIONARY CROSSOVER
database:
population_size: 25 # Larger population for more diversity
archive_size: 15
num_islands: 5 # More islands for parallel exploration
# Selection parameters - maximize exploration for combinatorial problem
elite_selection_ratio: 0.15
exploration_ratio: 0.6 # Very high exploration
exploitation_ratio: 0.25
# Feature dimensions - use built-in features
feature_dimensions: ["complexity", "diversity"]
feature_bins: 5
# Frequent migration helps combine good building blocks across islands
migration_interval: 3 # More frequent migration
migration_rate: 0.3 # Higher migration rate
# Evaluator Configuration
evaluator:
timeout: 30
max_retries: 2
cascade_evaluation: false # Simple evaluation, no cascade needed
parallel_evaluations: 4
use_llm_feedback: false
enable_artifacts: true
# Early stopping - disabled to allow full exploration
early_stopping_patience: 100 # Allow full run
convergence_threshold: 0.001
early_stopping_metric: "combined_score"
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