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      Multi-population parallel genetic algorithm using a new genetic representation for the euclidean traveling salesman problem

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      Author
      Kapanoğlu, M.
      Koç, İ. O.
      Kara, İ.
      Aktürk, Mehmet Selim
      Date
      2005
      Source Title
      Proceedings of the 35th International Conference on Computers and Industrial Engineering, ICC and IE 2005
      Publisher
      İstanbul Technical University
      Pages
      1047 - 1052
      Language
      English
      Type
      Conference Paper
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      Abstract
      This paper introduces a multi-population genetic algorithm (M-PPGA) using a new genetic representation, the kth-nearest neighbor representation, for Euclidean Traveling Salesman Problems. The proposed M-PPGA runs M greedy genetic algorithms on M separate populations, each with two new operators, intersection repairing and cheapest insert. The M-PPGA finds optimal or near optimal solutions by using a novel communication operator among individually converged populations. The algorithm generates high quality building blocks within each population; then, combines these blocks to build the optimal or near optimal solutions by means of the communication operator. The proposed M-PPGA outperforms the GAs that we know of as competitive with respect to running times and solution quality, over the considered test problems including the Turkey81.
      Keywords
      Kth-nearest neighbor representation
      Multi-population genetic algorithm
      Traveling salesman problem
      Building blockes
      Genetic representations
      Multi population
      Near-optimal solutions
      Nearest neighbors
      Parallel genetic algorithms
      Solution quality
      Genetic algorithms
      Industrial engineering
      Optimal systems
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      http://hdl.handle.net/11693/27343
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      • Department of Industrial Engineering 677
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