The progress rate of a self-adaptive evolution strategy is sub-optimal on ridge functions because the global step-size, denoted σ, becomes too small. On the parabolic ridge we conjecture that σ will stabilize when selection is unbiased towards larger or smaller step-sizes. On the sharp ridge, where the bias in selection is constant, σ will continue to decrease. We show that this is of practical interest because ridges can cause even the best solutions found by self-adaptation to be of little value on ridge problems where spatially close parameters tend to have similar values. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Lunacek, M., & Whitley, D. (2006). Searching for balance: Understanding self-adaptation on ridge functions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4193 LNCS, pp. 82–91). Springer Verlag. https://doi.org/10.1007/11844297_9
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