CMA-ES with Two-Point Step-Size Adaptation

Details CMA-ES with Two-Point Step-Size Adaptation CMA-ES with Two-Point Step-Size Adaptation

2008-05-02

arxiv.org/abs/0805.0231v4

Computer Science

Neural and Evolutionary Computing

Scientific paper

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

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