Analysis of Evolutionary Algorithms in Dynamic and Stochastic Environments
It provides a comprehensive overview for researchers in evolutionary computation, but is incremental as it synthesizes existing studies without new results.
This survey reviews theoretical developments in runtime analysis of evolutionary algorithms for dynamic and stochastic optimization problems, covering changes in objective functions, constraints, and noise models, and suggests future research directions.
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.