Abstract
Negative slope coefficient has been recently introduced and empirically proven a suitable hardness indicator for some well known genetic programming benchmarks, such as the even parity problem, the binomial-3 and the artificial ant on the Santa Fe trail. Nevertheless, the original definition of this measure contains several limitations. This paper points out some of those limitations, presents a new and more relevant definition of the negative slope coefficient and empirically shows the suitability of this new definition as a hardness measure for some genetic programming benchmarks, including the multiplexer, the intertwined spirals problem and the royal trees. © Springer-Verlag Berlin Heidelberg 2006.
Cite
CITATION STYLE
Vanneschi, L., Tomassini, M., Collard, P., & Vérel, S. (2006). Negative slope coefficient: A measure to characterize genetic programming fitness landscapes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3905 LNCS, pp. 178–189). https://doi.org/10.1007/11729976_16
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