Sub-Structural Niching in Non-Stationary Environments

Thumbnail Image

Date

Authors

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

Description

Niching enables a genetic algorithm (GA) to maintain diversity in a population. It is particularly useful when the problem has multiple optima where the aim is to find all or as many as possible of these optima. When the fitness landscape of a problem changes overtime, the problem is called non--stationary, dynamic or time--variant problem. In these problems, niching can maintain useful solutions to respond quickly, reliably and accurately to a change in the environment. In this paper, we present a niching method that works on the problem substructures rather than the whole solution, therefore it has less space complexity than previously known niching mechanisms. We show that the method is responding accurately when environmental changes occur.
Final version published in 2005 Australian Artificial Intelligence Conference, pp. 873--885

Keywords

Neural and Evolutionary Computing, Artificial Intelligence

Citation

URI

http://salesiana.dossiersoluciones.com/handle/123456789/32611

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/cs/0502022
 http://arxiv.org/abs/cs/0502022

Collections

arXiv