MultiGA: Leveraging Multi-Source Seeding in Genetic Algorithms

Wed, 01 Jan 2025 00:00:00 +0000

Different LLMs (like GPT-4, Claude, DeepSeek) are good at different things. Instead of picking one model and hoping for the best, MultiGA uses genetic algorithms—inspired by natural evolution—to combine outputs from multiple LLMs. It starts by generating diverse answers from several models, evaluates which ones are best, then “breeds” improved solutions by mixing and mutating them over multiple generations. We tested this on text-to-SQL generation, trip planning, and grad-level science questions, and found it matches or beats the best single model. Think of it as getting a second (and third, and fourth) opinion from different AI experts, then synthesizing their best ideas.

Haitao Zhu | Tharindu Cyril Weerasooriya

MultiGA: Leveraging Multi-Source Seeding in Genetic Algorithms