Towards Explainable Evolution Strategies with Large Language Models
This addresses the interpretability gap in advanced optimization algorithms for researchers and practitioners, though it is incremental as it combines existing methods.
The paper tackled the problem of making Evolution Strategies (ES) optimization more explainable by integrating them with Large Language Models (LLMs) to generate user-friendly summaries from optimization logs, demonstrating this on the Rastrigin function to enhance transparency.
This paper introduces an approach that integrates self-adaptive Evolution Strategies (ES) with Large Language Models (LLMs) to enhance the explainability of complex optimization processes. By employing a self-adaptive ES equipped with a restart mechanism, we effectively navigate the challenging landscapes of benchmark functions, capturing detailed logs of the optimization journey. The logs include fitness evolution, step-size adjustments and restart events due to stagnation. An LLM is then utilized to process these logs, generating concise, user-friendly summaries that highlight key aspects such as convergence behavior, optimal fitness achievements, and encounters with local optima. Our case study on the Rastrigin function demonstrates how our approach makes the complexities of ES optimization transparent. Our findings highlight the potential of using LLMs to bridge the gap between advanced optimization algorithms and their interpretability.