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  • Adapting the Genetic Algorithm to the Travelling Saleman Problem

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    Author(s)
    Pullan, W
    Griffith University Author(s)
    Pullan, Wayne J.
    Year published
    2003
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    Abstract
    The combination of local optimisation heuristics and genetic algorithms has been shown to be an effective approach for finding near-optimum solutions to the travelling salesman problem (TSP). In problem domains where the problem can be represented geometrically, such as networks and chemical structures, the combination of local optimisation operators and phenotype genetic operators has also been an effective approach. This paper evaluates the combination of local optimisation heuristics and phenotype genetic operators when applied to the TSP. The local optimisation heuristics reduce the search domain, while the phenotype ...
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    The combination of local optimisation heuristics and genetic algorithms has been shown to be an effective approach for finding near-optimum solutions to the travelling salesman problem (TSP). In problem domains where the problem can be represented geometrically, such as networks and chemical structures, the combination of local optimisation operators and phenotype genetic operators has also been an effective approach. This paper evaluates the combination of local optimisation heuristics and phenotype genetic operators when applied to the TSP. The local optimisation heuristics reduce the search domain, while the phenotype genetic operators eliminate the creation of invalid tours and also assist the generation of suboptimal schema. The implementation of the genetic algorithm is described and results presented.
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    Conference Title
    2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings
    Volume
    2
    Publisher URI
    http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9096
    DOI
    https://doi.org/10.1109/CEC.2003.1299781
    Copyright Statement
    © 2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
    Publication URI
    http://hdl.handle.net/10072/1782
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    • Conference outputs

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