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.
This work has been supported by project FEDER-MCYT TIC2003-04153 and by FICYT under grant BP04-021.
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González, M.A., Sierra, M., Vela, C.R., Varela, R. (2006). Genetic Algorithms Hybridized with Greedy Algorithms and Local Search over the Spaces of Active and Semi-active Schedules. In: Marín, R., Onaindía, E., Bugarín, A., Santos, J. (eds) Current Topics in Artificial Intelligence. CAEPIA 2005. Lecture Notes in Computer Science(), vol 4177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881216_25
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DOI: https://doi.org/10.1007/11881216_25
Publisher Name: Springer, Berlin, Heidelberg
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