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Using Genetic Algorithms to Optimize ACS-TSP

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Ant Algorithms (ANTS 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2463))

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Abstract

We propose the addition of Genetic Algorithms to Ant Colony System (ACS) appliedto improve performance. Two modifications are proposedandtested. The first algorithm is a hybrid between ACS-TSP anda Genetic Algorithm that encodes experimental variables in ants. The algorithm does not yieldimpro vedresults but offers concepts that can be used to improve the ACO algorithm. The second algorithm uses a Genetic Algorithm to evolve experimental variable values used in ACS-TSP. We have found that the performance of ACS-TSP can be improved by using the suggested values.

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References

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Author information

Authors and Affiliations

  1. School of Computer Science, Carleton University, 1125 Colonel By Drive, K1S 5B6, Ottawa, ON, Canada

    Marcin L. Pilat & Tony White

Authors
  1. Marcin L. Pilat
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  2. Tony White
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Editor information

Editors and Affiliations

  1. IRIDIA, Université de Bruxelles, CP 194/6 Avenue Franklin D. Roosevelt 50, 1050, Brussels, Belgium

    Marco Dorigo , Gianni Di Caro  & Michael Sampels ,  & 

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© 2002 Springer-Verlag Berlin Heidelberg

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Pilat, M.L., White, T. (2002). Using Genetic Algorithms to Optimize ACS-TSP. In: Dorigo, M., Di Caro, G., Sampels, M. (eds) Ant Algorithms. ANTS 2002. Lecture Notes in Computer Science, vol 2463. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45724-0_28

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  • DOI: https://doi.org/10.1007/3-540-45724-0_28

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44146-5

  • Online ISBN: 978-3-540-45724-4

  • eBook Packages: Springer Book Archive

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