Genetic algorithms with self-organized criticality for dynamic optimization problems

Date

2005

Advisors

Journal Title

Journal ISSN

ISSN

DOI

Volume Title

Publisher

IEEE Press

Type

Conference

Peer reviewed

Yes

Abstract

This paper proposes a genetic algorithm (GA) with random immigrants for dynamic optimization problems where the worst individual and its neighbours are replaced every generation. In this GA, the individuals interact with each other and, when their fitness is close, as in the case where the diversity level is low, one single replacement can affect a large number of individuals. This simple approach can take the system to a kind of self-organization behavior, known as self-organized criticality (SOC), which is useful to maintain the diversity of the population in dynamic environments and hence allows the GA to escape from local optima when the problem changes. The experimental results show that the proposed GA presents the phenomenon of SOC.

Description

Keywords

Citation

Tinos, R. and Yang, S. (2005) Genetic algorithms with self-organized criticality for dynamic optimization problems. Proceedings of the 2005 IEEE Congress on Evolutionary Computation, 3, pp. 2816-2823

Rights

Research Institute

Institute of Artificial Intelligence (IAI)