Analysis of Evolutionary Algorithms in Dynamic and Stochastic Environments

TL;DR

This survey reviews recent theoretical advances in analyzing evolutionary algorithms and bio-inspired methods for dynamic and stochastic optimization problems, highlighting key developments and future research directions.

Contribution

It provides a comprehensive overview of the latest theoretical results in runtime analysis for dynamic and stochastic environments, emphasizing evolutionary algorithms and ant colony optimization.

Findings

Summarizes recent theoretical studies on evolutionary algorithms in changing environments.

Highlights analysis of stochastic problems under various noise models.

Identifies future research directions in dynamic and stochastic optimization.

Abstract

Many real-world optimization problems occur in environments that change dynamically or involve stochastic components. Evolutionary algorithms and other bio-inspired algorithms have been widely applied to dynamic and stochastic problems. This survey gives an overview of major theoretical developments in the area of runtime analysis for these problems. We review recent theoretical studies of evolutionary algorithms and ant colony optimization for problems where the objective functions or the constraints change over time. Furthermore, we consider stochastic problems under various noise models and point out some directions for future research.