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Hybridizing Genetic Algorithms with Branch and Bound Techniques for the Resolution of the TSP

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Artificial Neural Nets and Genetic Algorithms

Abstract

In this paper some mixed techniques are outlined in order to combine the advantages of two very different methods for the resolution of combinatorial optimization problems (Genetic Algorithms and Branch and Bound Techniques), simultaneously avoiding their drawbacks. Due to the disparity between the basic techniques it is not suitable that they work at the same level so two models have been developed in which each technique assumes the role of being a tool of the other one. Parallelism is an important issue in these techniques.

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References

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Authors and Affiliations

  1. Dept. of Lenguajes y Ciencias de la Computación, Univ. of Málaga, Plaza de El Ejido s/n, E-29013, Málaga, España

    C. Cotta, J. F. Aldana, A. J. Nebro & J. M. Troya

Authors
  1. C. Cotta
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  2. J. F. Aldana
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  3. A. J. Nebro
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  4. J. M. Troya
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© 1995 Springer-Verlag/Wien

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Cotta, C., Aldana, J.F., Nebro, A.J., Troya, J.M. (1995). Hybridizing Genetic Algorithms with Branch and Bound Techniques for the Resolution of the TSP. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_73

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  • DOI: https://doi.org/10.1007/978-3-7091-7535-4_73

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-82692-8

  • Online ISBN: 978-3-7091-7535-4

  • eBook Packages: Springer Book Archive

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