Sub-structural niching in non-stationary environments

Authors:
Kumara Sastry;Hussein A. Abbass;David Goldberg
Affiliations:
Illinois Genetic Algorithms Laboratory, University of Illinois, Urbana, IL;Artificial Life and Adaptive Robotics Laboratory (ALAR), School of Information Technology and Electrical Engineering, University of New South Wales, Australian Defence Force Academy, Canberra, ACT ...;Illinois Genetic Algorithms Laboratory, University of Illinois, Urbana, IL
Venue:
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Year:
2004

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Abstract

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.