Journal of Computer Science and Technology

, Volume 27, Issue 5, pp 937–949 | Cite as

A Puzzle-Based Genetic Algorithm with Block Mining and Recombination Heuristic for the Traveling Salesman Problem

  • Pei-Chann ChangEmail author
  • Wei-Hsiu Huang
  • Zhen-Zhen Zhang
Regular Paper


In this research, we introduce a new heuristic approach using the concept of ant colony optimization (ACO) to extract patterns from the chromosomes generated by previous generations for solving the generalized traveling salesman problem. The proposed heuristic is composed of two phases. In the first phase the ACO technique is adopted to establish an archive consisting of a set of non-overlapping blocks and of a set of remaining cities (nodes) to be visited. The second phase is a block recombination phase where the set of blocks and the rest of cities are combined to form an artificial chromosome. The generated artificial chromosomes (ACs) will then be injected into a standard genetic algorithm (SGA) to speed up the convergence. The proposed method is called “Puzzle-Based Genetic Algorithm” or “p-ACGA”. We demonstrate that p-ACGA performs very well on all TSPLIB problems, which have been solved to optimality by other researchers. The proposed approach can prevent the early convergence of the genetic algorithm (GA) and lead the algorithm to explore and exploit the search space by taking advantage of the artificial chromosomes.


artificial chromosome blocks mining block recombination traveling salesman problem 


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Copyright information

© Springer Science+Business Media New York & Science Press, China 2012

Authors and Affiliations

  • Pei-Chann Chang
    • 1
    Email author
  • Wei-Hsiu Huang
    • 1
  • Zhen-Zhen Zhang
    • 2
  1. 1.Department of Information ManagementYuan Ze UniversityTaiwanChina
  2. 2.Department of Computer ScienceXiamen UniversityXiamenChina

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