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A Novel Multi-Population Genetic Algorithm for Multiple-Choice Multidimensional Knapsack Problems

  • Qian Zhou
  • Wenjian Luo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6382)

Abstract

In this paper, a novel Multi-Population Genetic Algorithm (MPGA) is proposed to solve the Multiple-choice Multidimensional Knapsack Problem (MMKP), a kind of classical combinatorial optimization problems. The proposed MPGA has two evolutionary populations and one archive population, and can effectively balance the search biases between the feasible space and the infeasible space. The experiment results demonstrate that the proposed MPGA is better than the existing algorithms, especially when the strength of constraints is relatively strong.

Keywords

Combinatorial Optimization Genetic Algorithm Multiple-choice Multidimensional Knapsack Problem 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Qian Zhou
    • 1
    • 2
  • Wenjian Luo
    • 1
    • 2
  1. 1.Nature Inspired Computation and Applications Laboratory, School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefeiChina
  2. 2.Anhui Key Laboratory of Software in Computing and CommunicationUniversity of Science and Technology of ChinaHefeiChina

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