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Lamarckian genetic algorithmsapplied to the aggregation of preferences

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  • Irène Charon
  • Olivier Hudry

Abstract

The problem that we deal with consists in aggregating a set of individual preferencesinto a collective linear order summarizing the initial set as accurately as possible. As thisproblem is NP-hard, we apply heuristics to find good approximate solutions. More precisely,we design a Lamarckian genetic algorithm by hybridizing some meta-heuristics (based onthe simulated annealing method or the noising method) with a genetic algorithm. For theproblems that we studied, the experiments show that such a hybridization brings improvementsto these already good methods. Copyright Kluwer Academic Publishers 1998

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  • Irène Charon & Olivier Hudry, 1998. "Lamarckian genetic algorithmsapplied to the aggregation of preferences," Annals of Operations Research, Springer, vol. 80(0), pages 281-297, January.
    • Handle: RePEc:spr:annopr:v:80:y:1998:i:0:p:281-297:10.1023/a:1018976217274
    DOI: 10.1023/A:1018976217274
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    Cited by:

    1. Ceberio, Josu & Mendiburu, Alexander & Lozano, Jose A., 2015. "The linear ordering problem revisited," European Journal of Operational Research, Elsevier, vol. 241(3), pages 686-696.
    2. Irène Charon & Olivier Hudry, 2010. "An updated survey on the linear ordering problem for weighted or unweighted tournaments," Annals of Operations Research, Springer, vol. 175(1), pages 107-158, March.
    3. Thierry Denœux & Marie-Hélène Masson, 2012. "Evidential reasoning in large partially ordered sets," Annals of Operations Research, Springer, vol. 195(1), pages 135-161, May.
    4. Charon, Irene & Hudry, Olivier, 2001. "The noising methods: A generalization of some metaheuristics," European Journal of Operational Research, Elsevier, vol. 135(1), pages 86-101, November.

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