LS-CMA-ES: a Second-order algorithm for Covariance Matrix Adaptation
Anne Auger, Marc Schoenauer and Nicolas Vanhaecke
In: PPSN 2004, Sept. 2004, Birmingham.
Evolution Strategies, a class of Evolutionary Algorithms based on
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 update of the covariance matrix
is based on a quadratic approximation of the target function,
obtained by some Least-Square minimization.
criterion is designed to detect situations where the approximation is not
accurate enough, and original Covariance Matrix Adaptation (CMA) should rather be directly used.
The resulting algorithm is experimentally validated on benchmark functions,
outperforming CMA-ES on a large class of problems.