Abstract
We define a set of measures that capture some different aspects of neutrality in evolutionary algorithms fitness landscapes from a qualitative point of view. If considered all together, these measures offer a rather complete picture of the characteristics of fitness landscapes bound to neutrality and may be used as broad indicators of problem hardness. We compare the results returned by these measures with the ones of negative slope coefficient, a quantitative measure of problem hardness that has been recently defined and with success rate statistics on a well known genetic programming benchmark: the multiplexer problem. In order to efficaciously study the search space, we use a sampling technique that has recently been introduced and we show its suitability on this problem. © Springer-Verlag Berlin Heidelberg 2007.
Cite
CITATION STYLE
Vanneschi, L., Tomassini, M., Collard, P., Vérel, S., Pirola, Y., & Mauri, G. (2007). A comprehensive view of fitness landscapes with neutrality and fitness clouds. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4445 LNCS, pp. 241–250). Springer Verlag. https://doi.org/10.1007/978-3-540-71605-1_22
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