Fit to Fling: Genetic Prompt Optimization for the LLMs4PCG Competition

This paper presents a genetic search-based approach to automatic prompt optimization for the 2025 LLMs4PCG competition. Large language models can enhance the longevity and replayability of games through procedural content generation, but reliance on proprietary third-party APIs raises concerns about cost and sustainability. Open-weight models return control to developers, though often with reduced capabilities. We propose a genetic algorithm that automatically optimizes prompts to help close this performance gap on certain tasks. Experimental results and competition benchmarks suggest this is a promising step toward enabling practical use of smaller, open-weight models for PCG in games.