Genetic algorithms with memory- and elitism-based immigrants in dynamic environments.

Date

2008

Advisors

Journal Title

Journal ISSN

ISSN

1063-6560

Volume Title

Publisher

MIT Press.

Type

Article

Peer reviewed

Abstract

In recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The random immigrants scheme addresses dynamic environments by maintaining the population diversity while the memory scheme aims to adapt genetic algorithms quickly to new environments by reusing historical information. This paper investigates a hybrid memory and random immigrants scheme, called memory-based immigrants, and a hybrid elitism and random immigrants scheme, called elitism-based immigrants, for genetic algorithms in dynamic environments. In these schemes, the best individual from memory or the elite from the previous generation is retrieved as the base to create immigrants into the population by mutation. This way, not only can diversity be maintained but it is done more efficiently to adapt genetic algorithms to the current environment. Based on a series of systematically constructed dynamic problems, experiments are carried out to compare genetic algorithms with the memory-based and elitism-based immigrants schemes against genetic algorithms with traditional memory and random immigrants schemes and a hybrid memory and multi-population scheme. The sensitivity analysis regarding some key parameters is also carried out. Experimental results show that the memory-based and elitism-based immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments.

Description

Keywords

Genetic algorithms (GAs), Dynamic optimization problems (DOPs), Memory, Random immigrants, Memory-based immigrants, Elitism-based immigrants

Citation

Yang, S. (2008) Genetic algorithms with memory- and elitism-based immigrants in dynamic environments. Evolutionary Computation, 16(3), Fall 2008, pp. 385-416.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)