Feature selection via competitive levy flights

Evolutionary meta-heuristics are designed for optimization using population with selection and mutation operators. Novelty of our approach is based on competition of various operators from mutation portfolio. Resulting meta-heuristic is successfully tested on the feature selection task: searching for a sparse sub-model having the best possible value by means of information criteria. Beginning with such statistical formulation we obtain an NP-hard optimization task which can be efficiently solved via meta-heuristic approach. We demonstrate how meta-heuristic optimization combined with statistics can enhance machine learning models and therefore is useful in Computational Intelligence in general.