Motivated by an experimental problem involving the identification of effective drug combinations drawn from a non-static drug library, this paper examines evolutionary algorithm strategies for dealing with changes of variables. We consider four standard techniques from dynamic optimization, and propose one new technique. The results show that only little additional diversity needs to be introduced into the population when changing a small number of variables, while changing many variables or optimizing a rugged landscape requires often a restart of the optimization process. © 2010 Springer-Verlag.
CITATION STYLE
Allmendinger, R., & Knowles, J. (2010). Evolutionary optimization on problems subject to changes of variables. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6239 LNCS, pp. 151–160). https://doi.org/10.1007/978-3-642-15871-1_16
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