Super-fit Multicriteria Adaptive Differential Evolution

• Published in: 2013 IEEE Congress on Evolutionary Computation pp. 1678 - 1685
• Main Authors: Caraffini, Fabio, Neri, Ferrante, Jixiang Cheng, Gexiang Zhang, Picinali, Lorenzo, Iacca, Giovanni, Mininno, Ernesto
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2013
• Subjects: Covariance matrices; Memetics; Optimization; Signal processing algorithms; Sociology; Vectors
• ISBN: 1479904538; 9781479904532
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/CEC.2013.6557763

This paper proposes an algorithm to solve the CEC2013 benchmark. The algorithm, namely Super-fit Multicriteria Adaptive Differential Evolution (SMADE), is a Memetic Computing approach based on the hybridization of two algorithmic schemes according to a super-fit memetic logic. More specifically, the Covariance Matrix Adaptive Evolution Strategy (CMAES), run at the beginning of the optimization process, is used to generate a solution with a high quality. This solution is then injected into the population of a modified Differential Evolution, namely Multicriteria Adaptive Differential Evolution (MADE). The improved solution is super-fit as it supposedly exhibits a performance a way higher than the other population individuals. The super-fit individual then leads the search of the MADE scheme towards the optimum. Unimodal or mildly multimodal problems, even when non-separable and ill-conditioned, tend to be solved during the early stages of the optimization by the CMAES. Highly multi-modal optimization problems are efficiently tackled by SMADE since the MADE algorithm (as well as other Differential Evolution schemes) appears to work very well when the search is led by a super-fit individual.