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. © Springer-Verlag Berlin Heidelberg 2003.
Cite
CITATION STYLE
Yu, H., Wu, A. S., Lin, K. C., & Schiavone, G. (2003). Adaptation of length in a nonstationary environment. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2724, 1541–1553. https://doi.org/10.1007/3-540-45110-2_25
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