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A Genetic Algorithm Hybridized with the Discrete Lagrangian Method for Trap Escaping

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Learning and Intelligent Optimization (LION 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6683))

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Abstract

This paper introduces a genetic algorithm enhanced with a trap escaping strategy derived from the dual information presented as discrete Lagrange multipliers. When the genetic algorithm is trapped into a local optima, the Discrete Lagrange Multiplier method is called for the best individual found. The information provided by the Lagrangian method is unified, in the form of recombination, with the one from the last population of the genetic algorithm. Then the genetic algorithm is restarted with this new improved configuration. The proposed algorithm is tested on the winner determination problem. Experiments are conducted using instances generated with the combinatorial auction test suite system. The results show that the method is viable.

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Authors and Affiliations

  1. “Al.I.Cuza” University of Iasi, Romania

    Madalina Raschip & Cornelius Croitoru

Authors
  1. Madalina Raschip
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  2. Cornelius Croitoru
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Editors and Affiliations

  1. Departmento de Computation, Centro de Investigacion y de Estudios, Avanzados del Instituto Politecnico Nacional (CINVESTAV-IPN), Av. IPN No. 2508, Col. San Pedro Zacatenco, D.F. 0360, Mexico, Mexico

    Carlos A. Coello Coello

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Raschip, M., Croitoru, C. (2011). A Genetic Algorithm Hybridized with the Discrete Lagrangian Method for Trap Escaping. In: Coello, C.A.C. (eds) Learning and Intelligent Optimization. LION 2011. Lecture Notes in Computer Science, vol 6683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25566-3_26

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  • DOI: https://doi.org/10.1007/978-3-642-25566-3_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25565-6

  • Online ISBN: 978-3-642-25566-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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