darwinian-evolver

You can optimize prompt engineering templates by running an evolutionary search loop that iteratively mutates candidate prompts and retains the variants that score best against your defined fitness function.

Yes, you can tune SQL queries and regex patterns by defining an organism and an evaluator with trainable and holdout failure cases, allowing the evolutionary loop to mutate and validate candidate artifacts.

Yes, you need a fitness function to improve code snippets, as the evolutionary search loop requires a measurable fitness signal from an evaluator to score and select mutated code block candidates.

Evolutionary code optimization works with OpenRouter and OpenAI by using an LLM-driven mutator that generates candidate mutations through the provided OpenRouter driver or the upstream darwinian_evolver workflow.

The best way to prevent overfitting when evolving regex patterns is to separate your evaluator's examples into trainable and holdout failure cases, ensuring mutations generalize beyond the learning set.