Differential evolution with self-adaptive populations

Teo, Jason Tze Wi (2005) Differential evolution with self-adaptive populations. In: 9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, 14-16 September 2005, La Trobe Univ, Melbourne, Australia.

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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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords: Parameterless evolutionary algorithms, Differential evolution, Self-adaptation, Population dynamics, Parameter encoding, Evolutionary optimization, Natural computation
Subjects: ?? QA150-272.5 ??
Divisions: SCHOOL > School of Engineering and Information Technology
Depositing User: Unnamed user with email storage.bpmlib@ums.edu.my
Date Deposited: 18 Oct 2011 03:07
Last Modified: 29 Dec 2014 08:19
URI: http://eprints.ums.edu.my/id/eprint/977

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