Improving Evolution Strategies through Active Covariance Matrix Adaptation

This paper proposes a novel modification to the derandomised covariance matrix adaptation algorithm used in connection with evolution strategies. In existing variants of that algorithm, only information gathered from successful offspring candidate solutions contributes to the adaptation of the covariance matrix, while old information passively decays. We propose to use information about unsuccessful offspring candidate solutions in order to actively reduce variances of the mutation distribution in unpromising directions of the search space. The resulting strategy is referred to as Active-CMA-ES. In experiments on a standard suite of test functions, Active-CMA-ES consistently outperforms other strategy variants.