The dispersion metric and the CMA evolution strategy

摘要

An algorithm independent metric is introduced that measures the dispersion of a uniform random sample drawn from the top ranked percentiles of the search space. A low dispersion function is one where the dispersion decreases as the sample is restricted to better regions of the search space. A high dispersion function is one where dispersion stay constant or increases as the sample is restricted to better regions of the search space. This distinction can be used to explain why the CMA Evolution Strategy is more efficient on some multimodal problems than on others.