Evolution Strategy

Evolution strategies (ES) are gradient-free optimization algorithms inspired by biological evolution, primarily used to find optimal solutions in complex, high-dimensional spaces where gradient information is unavailable or unreliable. Current research focuses on improving ES efficiency and applicability, particularly through adaptive re-evaluation methods for noisy functions, integration with deep reinforcement learning and large language models for enhanced performance and explainability, and the development of distributed and communication-efficient variants for large-scale problems. ES's robustness and adaptability make them valuable tools across diverse fields, including robotics, machine learning, and engineering design, offering powerful alternatives to gradient-based methods in challenging optimization scenarios.