CMA-ES with Two-Point Step-Size Adaptation
Neural and Evolutionary Computing
2008-12-18 v4
Abstract
We combine a refined version of two-point step-size adaptation with the covariance matrix adaptation evolution strategy (CMA-ES). Additionally, we suggest polished formulae for the learning rate of the covariance matrix and the recombination weights. In contrast to cumulative step-size adaptation or to the 1/5-th success rule, the refined two-point adaptation (TPA) does not rely on any internal model of optimality. In contrast to conventional self-adaptation, the TPA will achieve a better target step-size in particular with large populations. The disadvantage of TPA is that it relies on two additional objective function
Keywords
Cite
@article{arxiv.0805.0231,
title = {CMA-ES with Two-Point Step-Size Adaptation},
author = {Nikolaus Hansen},
journal= {arXiv preprint arXiv:0805.0231},
year = {2008}
}