Genetic algorithms for changing environments

TLDR

Genetic algorithms perform an adaptive search by maintaining a population of candidate solutions that are allocated dynamically to promising regions of the search space, and their distributed nature provides a natural source of power for searching in changing environments, but rapid convergence can reduce their ability to identify newly attractive regions as the environment changes. The paper proposes a modification of the standard generational genetic algorithm to maintain diversity for tracking changing response surfaces. The modification is implemented by adjusting the algorithm to preserve diversity during evolution, enabling it to respond to changes in the response surface. An experimental study shows some promise for the new technique.

Abstract

Genetic algorithms perform an adaptive search by maintaining a population of candidate solutions that are allocated dynamically to promising regions of the search space. The distributed nature of the genetic search provides a natural source of power for searching in changing environments. As long as sufficient diversity remains in the population the genetic algorithm can respond to a changing response surface by reallocating future trials. However, the tendency of genetic algorithms to converge rapidly reduces their ability to identify regions of the search space that might suddenly become more attractive as the environment changes. This paper presents a modification of the standard generational genetic algorithm that is designed to maintain the diversity required to track a changing response surface. An experimental study shows some promise for the new technique.