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# EVOLVE-BLOCK-START
"""
K-Module Pipeline Configuration Problem
This problem demonstrates a scenario where iterative refinement struggles
but evolutionary search with crossover excels.
The task is to find the correct configuration for a 4-component data
processing pipeline. Each module has 5 possible options, creating a
search space of 5^4 = 625 possible combinations.
The key challenge: there's no gradient information. Getting 3/4 modules
correct gives the same partial feedback as 1/4 - you don't know WHICH
modules are correct.
"""
def configure_pipeline():
"""
Configure a data processing pipeline with 4 independent modules.
Each module choice is independent - changing one doesn't affect
what's optimal for others. This creates a "needle in haystack"
problem for iterative refinement but is solvable efficiently
by evolutionary crossover.
Returns:
dict: Configuration with keys 'loader', 'preprocess', 'algorithm', 'formatter'
"""
# Available options for each module:
# 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']
# Initial guess - likely not optimal
config = {
'loader': 'json_reader', # Try different loaders
'preprocess': 'standardize', # Try different preprocessing
'algorithm': 'mergesort', # Try different algorithms
'formatter': 'xml', # Try different formatters
}
return config
# EVOLVE-BLOCK-END
def run_pipeline():
"""Run the pipeline configuration (entry point for evaluator)."""
return configure_pipeline()
if __name__ == "__main__":
config = run_pipeline()
print(f"Pipeline configuration: {config}")
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