Self-Adaptive Niching CMA-ES with Mahalanobis Metric

Existing niching techniques commonly use the Euclidean distance metric in the decision space for the classification of feasible solutions to the niches under formation. This approach is likely to encounter problems in high-dimensional landscapes with non-isotropic basins of attraction. Here we consider niching with the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and introduce the Mahalanobis distance metric into the niching mechanism, aiming to allow a more accurate spatial classification, based on the ellipsoids of the distribution, rather than hyper-spheres of the Euclidean metric. This is tested with the CMA`+,

• Type: Conference
• Year: 2007
• Where: CEC
• Authors: Ofer M. Shir, Michael Emmerich, Thomas Bäck
• Keywords: Artificial Intelligence, CEC 2007, Distance Metric, Euclidean Distance Metric, Niche Radius