Genetic algorithms hybridized with greedy algorithms and local search over the spaces of active and semi-active schedules

Authors:
Miguel A. González;María Sierra;Camino R. Vela;Ramiro Varela
Affiliations:
Department of Computing Artificial Intelligence Center, University of Oviedo, Gijón, Spain;Department of Computing Artificial Intelligence Center, University of Oviedo, Gijón, Spain;Department of Computing Artificial Intelligence Center, University of Oviedo, Gijón, Spain;Department of Computing Artificial Intelligence Center, University of Oviedo, Gijón, Spain
Venue:
CAEPIA'05 Proceedings of the 11th Spanish association conference on Current Topics in Artificial Intelligence
Year:
2005

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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.