Please use this identifier to cite or link to this item: https://doi.org/10.1080/088395101750363966
Title: A novel hybrid genetic algorithm using local optimizer based on heuristic pattern move
Authors: Xu, Y.G. 
Li, G.R. 
Wu, Z.P.
Issue Date: Aug-2001
Citation: Xu, Y.G., Li, G.R., Wu, Z.P. (2001-08). A novel hybrid genetic algorithm using local optimizer based on heuristic pattern move. Applied Artificial Intelligence 15 (7) : 601-631. ScholarBank@NUS Repository. https://doi.org/10.1080/088395101750363966
Abstract: A new hybrid genetic algorithm with the significant improvement of convergence performance is proposed in this study. This algorithm comes from the incorporation of a modified microgenetic algorithm with a local optimizer based on the heuristic pattern move. The hybridization process is implemented by replacing the two worst individuals in the offspring obtained from the conventional genetic operations with two new individuals generated from the local optimizer in each generation. Some implementation-related problems such as the selection of control parameters in the local optimizer are addressed in detail. This new algorithm has been examined using six benchmarking functions, and is compared with the conventional genetic algorithms without the local optimizer incorporated, as well as the hybrid algorithms incorporated with the hill-climbing method in terms of convergence performance. The results show that the proposed hybrid algorithm is more effective and efficient to obtain the global optimum. It takes about 6.4%-74.4% of the number of generations normally required by the conventional genetic algorithms to obtain the global optimum, while the computation cost for reproducing each new generation has hardly increased compared to the conventional genetic algorithms. Another advantage of this new algorithm is the implementation process is very simple and straightforward. There are no extra function evaluations and other complex calculations involved in the added local optimizer as well as in the hybridization process. This makes the new algorithm easy to be incorporated with the existing software packages of genetic algorithms so as to further improve their performance. As an engineering example, this new algorithm is applied for the detection of a crack in a composite plate, which demonstrates its effectiveness in solving engineering practical problems.
Source Title: Applied Artificial Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/114609
ISSN: 08839514
DOI: 10.1080/088395101750363966
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