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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"

	# 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"