Adaptation Of Length In A Nonstationary Environment
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.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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Yu, Han; Wu, Annie S.; and Lin, Kuo Chi, "Adaptation Of Length In A Nonstationary Environment" (2003). Scopus Export 2000s. 2005.