A variable-grouping based genetic algorithm for large-scale integer programming

Authors:
Zhongsheng Hua;Feihua Huang
Affiliations:
Department of Information Management & Decision Science, School of Management, University of Science & Technology of China, Hefei, Anhui 230026, People's Republic of China;Department of Information Management & Decision Science, School of Management, University of Science & Technology of China, Hefei, Anhui 230026, People's Republic of China
Venue:
Information Sciences: an International Journal
Year:
2006

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Abstract

To effectively reduce the dimensionality of search space, this paper proposes a variable-grouping based genetic algorithm (VGGA) for large-scale integer programming problems (IPs). The VGGA first groups IP's decision variables based on the optimal solution to the IP's continuous relaxation problem, and then applies a standard genetic algorithm (GA) to the subproblem for each group of variables. We compare the VGGA with the standard GA and GAs based on even variable-grouping by applying them to solve randomly generated convex quadratic knapsack problems and integer knapsack problems. Numerical results suggest that the VGGA is superior to the standard GA and GAs based on even variable-grouping both on computation time and solution quality.