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Genetic Algorithms Hybridized with Greedy Algorithms and Local Search over the Spaces of Active and Semi-active Schedules

  • Miguel A. González
  • María Sierra
  • Camino R. Vela
  • Ramiro Varela
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4177)

Abstract

The Job Shop Scheduling is a paradigm of Constraint Satisfaction Problems that has interested to researchers over the last years. In this work we propose a Genetic Algorithm hybridized with a local search method that searches over the space of semi-active schedules and a heuristic seeding method that generates active schedules stochastically. We report results from an experimental study over a small set of selected problem instances of common use, and also over a set of big problem instances that clarify the influence of each method in the Genetic Algorithm performance.

Keywords

Genetic Algorithm Search Space Local Search Problem Instance Critical Path 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Miguel A. González
    • 1
  • María Sierra
    • 1
  • Camino R. Vela
    • 1
  • Ramiro Varela
    • 1
  1. 1.Department of Computing Artificial Intelligence CenterUniversity of OviedoGijónSpain

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