Differential evolution with self-adaptive populations

Authors:
Jason Teo
Affiliations:
AI Research Group, School of Engineering and Information Technology, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Year:
2005

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Abstract

In this paper, we present a first attempt at self-adapting the population size parameter in addition to self-adapting crossover and mutation rates for the Differential Evolution (DE) algorithm. The objective is to demonstrate the feasibility of self-adapting the population size parameter in DE. Using De Jong's F1-F5 benchmark test problems, we showed that DE with self-adaptive populations produced highly competitive results compared to a conventional DE algorithm with static populations. In addition to reducing the number of parameters used in DE, the proposed algorithm performed better in terms of best solution found than the conventional DE algorithm for one of the test problems. It was also found that that an absolute encoding methodology for self-adapting population size in DE produced results with greater optimization reliability compared to a relative encoding methodology.