darwinian-evolver

You can optimize regex patterns or SQL queries by running an evolutionary search loop that iterates against a fitness function. This approach automates the search for better-performing solutions by mutating and evaluating candidate code snippets.

Evolutionary search for prompt engineering is a genetic algorithm-inspired process where prompts are treated as organisms. The system mutates and evaluates them against a fitness function to systematically discover optimized prompt variations.

Yes, you need Python 3.11 or later to run darwinian-evolver for code optimization. You also need specific API keys or environment variables configured to enable LLM access for the evolutionary loop.

Yes, you can define custom fitness functions for code optimization by specifying custom problem definitions. You configure organisms, evaluators, and mutators to guide the evolutionary search loop toward your specific optimization goals.

Automating prompt optimization with a genetic algorithm is best done by defining custom organisms, evaluators, and mutators. The system uses an LLM-driven evolutionary search loop to automatically evolve and optimize prompts against your fitness function.