Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7645
Title: Efficiency speed-up strategies for evolutionary computation: An adaptive implementation
Authors: Prof. LEUNG Kwong Sak 
Sun, Jian-Yong 
Xu, Zong-Ben 
Issue Date: 2002
Source: Engineering Computations (Swansea, Wales), 2002, vol. 19 (3-4), pp. 272 - 304
Journal: Engineering Computations (Swansea, Wales) 
Abstract: In this paper, a set of safe adaptive genetic algorithms (sGAs) is proposed based on the Splicing/Decomposable encoding scheme and the efficient speed-up strategies developed by Xu et al. The proposed algorithms implement the self-adaptation of the problem representation, selection and recombination operators at the levels of population, individual and component which commendably balance the conflicts between "reliability" and "efficiency", as well as "exploitation" and "exploration" existed in the evolutionary algorithms. It is shown that the algorithms converge to the optimum solution in probability one. The proposed sGAs are experimentally compared with the classical genetic algorithm (CGA), non-uniform genetic algorithm (nGA) proposed by Michalewicz, forking genetic algorithm (FGA) proposed by Tsutsui et al. and the classical evolution programming (CEP). The experiments indicate that the new algorithms perform much more efficiently than CGA and FGA do, comparable with the real-coded GAs - nGA and CEP. All the algorithms are further evaluated through an application to a difficult real-life application problem: the inverse problem of fractal encoding related to fractal image compression technique. The results for the sGA is better than those of CGA and FGA, and has the same, sometimes better performance compared to those of nGA and CEP.
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/7645
ISSN: 02644401
DOI: 10.1108/02644400210423963
Appears in Collections:Applied Data Science - Publication

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