Evolutionary adaptation of the differential evolution control parameters

Authors:
M. G. Epitropakis;V. P. Plagianakos;M. N. Vrahatis
Affiliations:
Computational Intelligence Laboratory, Department of Mathematics, University of Patras Artificial Intelligence Research Center, University of Patras, Patras, Greece;Computational Intelligence Laboratory, Department of Mathematics, University of Patras Artificial Intelligence Research Center, University of Patras, Patras, Greece;Computational Intelligence Laboratory, Department of Mathematics, University of Patras Artificial Intelligence Research Center, University of Patras, Patras, Greece
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

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Abstract

This papers proposes a novel self-adaptive scheme for the evolution of crucial control parameters in Evolutionary Algorithms. More specifically, we suggest to utilize the Differential Evolution algorithm to endemically evolve its own control parameters. To achieve this, two simultaneous instances of Differential Evolution are used, one of which is responsible for the evolution of the crucial user-defined mutation and recombination constants. This self-adaptive Differential Evolution algorithm alleviates the need of tuning these user-defined parameters while maintains the convergence properties of the original algorithm. The evolutionary self-adaptive scheme is evaluated through several well-known optimization benchmark functions and the experimental results indicate that the proposed approach is promising.