LS-CMA-ES: A Second-Order Algorithm for Covariance Matrix Adaptation

  title={LS-CMA-ES: A Second-Order Algorithm for Covariance Matrix Adaptation},
  author={Anne Auger and Marc Schoenauer and Nicolas Vanhaecke},
Evolution Strategies, Evolutionary Algorithms based on Gaussian mutation and deterministic selection, are today considered the best choice as far as parameter optimization is concerned. However, there are multiple ways to tune the covariance matrix of the Gaussian mutation. After reviewing the state of the art in covariance matrix adaptation, a new approach is proposed, in which the covariance matrix adaptation method is based on a quadratic approximation of the target function obtained by some… CONTINUE READING