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Knowledge Incorporation in Evolutionary Computation pp 37–55Cite as

A Cultural Algorithm for Solving the Job Shop Scheduling Problem

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 167))

Summary

In this chapter, we propose an approach for solving the job shop scheduling problem using a cultural algorithm. Cultural algorithms are evolutionary computation methods that extract domain knowledge during the evolutionary process. Additional to this extracted knowledge, the proposed approach also uses domain knowledge given “a priori” (based on specific domain knowledge available for the job shop scheduling problem). The proposed approach is compared with respect to a Greedy Randomized Adaptive Search Procedure and to a Parallel Genetic Algorithm. The cultural algorithm proposed is able to produce competitive results with respect to the two approaches previously indicated at a significantly lower computational cost than at least one of them and without using any sort of parallel processing.

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Authors and Affiliations

  1. CINVESTAV-IPN, Evolutionary Computation Group, Departamento de Ingeniería Eléctrica, Sección de Computación, Av. Instituto Politécnico Nacional No. 2508, Col. San Pedro Zacatenco, México, D.F. 07300, México

    Ricardo Landa Becerra & Carlos A. Coello Coello

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  1. Ricardo Landa Becerra
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  2. Carlos A. Coello Coello
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Editors and Affiliations

  1. Honda Research Institute Europe GmbH, Carl-Legien-Str.30, D-63073, Offenbach/Main, Germany

    Yaochu Jin

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Becerra, R.L., Coello, C.A.C. (2005). A Cultural Algorithm for Solving the Job Shop Scheduling Problem. In: Jin, Y. (eds) Knowledge Incorporation in Evolutionary Computation. Studies in Fuzziness and Soft Computing, vol 167. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44511-1_3

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  • DOI: https://doi.org/10.1007/978-3-540-44511-1_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-06174-5

  • Online ISBN: 978-3-540-44511-1

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