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Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/2548
Title: Adaptive genetic algorithm and quasi-parallel genetic algorithm : application to knapsack problem
Authors: Szeto, Kwok-Yip
Zhang, Jian
Keywords: Adaptive genetic algorithm
Knapsack problem
Issue Date: Jun-2005
Citation: 5th International Conference on Large-Scale Scientific Computing, 2005 (LNCS 3743) p. 189-96
Abstract: A new adaptive genetic algorithm using mutation matrix is introduced and implemented in a single computer using the quasi-parallel time sharing algorithm for the solution of the zero/one knapsack problem. The mutation matrix M(t) is constructed using the locus statistics and the fitness distribution in a population A(t) with N rows and L columns, where N is the size of the population and L is the length of the encoded chromosomes. The mutation matrix is parameter free and adaptive as it is time dependent andcaptures the accumulated information in the past generation. Two strategies of evolution, mutation by row (chromosome), and mutation by column (locus) are discussed. Time sharing experiment on these two strategies is performed on a single computer for solving the knapsack problem. Based on the investment frontier of time allocation, the optimal configuration for solving the knapsack problem is found.
Rights: The original publication is available at http://www.springerlink.com/. Please use the appropriate URL and/or DOI for the article. The access script to link to it is: http://library.ust.hk/cgi/db/doi.pl?10.1007/11666806_20
URI: http://hdl.handle.net/1783.1/2548
Appears in Collections:PHYS Conference Papers

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