A gene expression programming framework for evolutionary design of metaheuristic algorithms
Metaheuristic algorithms have successfully tackled many difficult and ill conditioned optimization problems. Nevertheless, performance of these methods is subjected to the complexity and fitness landscape of the problem at hand. Accordingly, designing metaheuristic algorithms that work well on a variety of optimization problems is not a trivial task. In this study, we introduce a novel framework for improving generalization capability of the metaheuristic algorithms based on the notion of gene expression programming (GEP) The proposed framework introduces a modified GEP (MGEP) in order to adaptively design search operators of a metaheuristic algorithm. During evolution process, a multi criteria procedure determines the search operators that are preferable and can obtain high accuracy results. Performance of the proposed approach is empirically evaluated on CEC 2013 test suite. The obtained results confirm that the evolved metaheuristic algorithms by this framework perform similarly to or better than the standard versions.
2016 IEEE Congress on Evolutionary Computation (CEC)