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A New Genetic Algorithm Encoding for Coalition Structure Generation Problems

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  • Juan Pablo Contreras
  • Paul Bosch
  • Mauricio Varas
  • Franco Basso

Abstract

Genetic algorithms have proved to be a useful improvement heuristic for tackling several combinatorial problems, including the coalition structure generation problem. In this case, the focus lies on selecting the best partition from a discrete set. A relevant issue when designing a Genetic algorithm for coalition structure generation problems is to choose a proper genetic encoding that enables an efficient computational implementation. In this paper, we present a novel hybrid encoding, and we compare its performance against several genetic encoding proposed in the literature. We show that even in difficult instances of the coalition structure generation problem, the proposed approach is a competitive alternative to obtaining good quality solutions in reasonable computing times. Furthermore, we also show that the encoding relevance increases as the number of players increases.

Suggested Citation

  • Juan Pablo Contreras & Paul Bosch & Mauricio Varas & Franco Basso, 2020. "A New Genetic Algorithm Encoding for Coalition Structure Generation Problems," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, April.
    • Handle: RePEc:hin:jnlmpe:1203248
    DOI: 10.1155/2020/1203248
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    Cited by:

    1. Basso, Franco & Basso, Leonardo J. & Rönnqvist, Mikael & Weintraub, Andres, 2021. "Coalition formation in collaborative production and transportation with competing firms," European Journal of Operational Research, Elsevier, vol. 289(2), pages 569-581.

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