Adaptation of length in a nonstationary environment

Authors:
Han Yu;Annie S. Wu;Kuo-Chi Lin;Guy Schiavone
Affiliations:
School of EECS, University of Central Florida, Orlando, FL;School of EECS, University of Central Florida, Orlando, FL;Institute for Simulation and Training, University of Central Florida, Orlando, FL;Institute for Simulation and Training, University of Central Florida, Orlando, FL
Venue:
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Year:
2003

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Abstract

In this paper, we examine the behavior of a variable length GA in a nonstationary problem environment. Results indicate that a variable length GA is better able to adapt to changes than a fixed length GA. Closer examination of the evolutionary dynamics reveals that a variable length GA can in fact take advantage of its variable length representation to exploit good quality building blocks after a change in the problem environment.