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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/1127

Title: Incremental evolution strategy for function optimization
Authors: Guan, SU
Mo, W
Keywords: Evolution strategy
Function optimization
Incremental evolution
Particle swarm optimization
Publication Date: 2006
Publisher: IOS Press
Citation: International Journal of Hybrid Intelligent Systems. 3 (4) 187-203
Abstract: This paper presents a novel evolutionary approach for function optimization Incremental Evolution Strategy (IES). Two strategies are proposed. One is to evolve the input variables incrementally. The whole evolution consists of several phases and one more variable is focused in each phase. The number of phases is equal to the number of variables in maximum. Each phase is composed of two stages: in the single-variable evolution (SVE) stage, evolution is taken on one independent variable in a series of cutting planes; in the multi-variable evolving (MVE) stage, the initial population is formed by integrating the populations obtained by the SVE and the MVE in the last phase. And the evolution is taken on the incremented variable set. The other strategy is a hybrid of particle swarm optimization (PSO) and evolution strategy (ES). PSO is applied to adjust the cutting planes/hyper-planes (in SVEs/MVEs) while (1+1)-ES is applied to searching optima in the cutting planes/hyper-planes. The results of experiments show that the performance of IES is generally better than that of three other evolutionary algorithms, improved normal GA, PSO and SADE_CERAF, in the sense that IES finds solutions closer to the true optima and with more optimal objective values.
URI: http://bura.brunel.ac.uk/handle/2438/1127
ISSN: 1448-5869
Appears in Collections:School of Engineering and Design Research papers
Electronic and Computer Engineering

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