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International Conference on Rough Sets and Current Trends in Computing

RSCTC 1998: Rough Sets and Current Trends in Computing pp 271–274Cite as

Learning with Delayed Rewards in Ant Systems for the Job-Shop Scheduling Problem

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1424))

Abstract

We apply the idea of learning with delayed rewards to improve performance of the Ant System. We will mention different mechanisms of delayed rewards in the Ant Algorithm (AA). The AA for JSP was first applied in classical form by A. Colorni and M. Dorigo. We adapt an idea of an evolution of the algorithm itself using the methods of the learning process. We accentuate the co-operation and stigmergy effect in this algorithm. We propose the optimal values of the parameters used in this version of the AA, derived as a result of our experiments.

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References

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Author information

Authors and Affiliations

  1. Institute of Computer Science, University of Silesia, ul. Zeromskiego 3, 41-200, Sosnowiec, Poland

    Urszula Boryczka

Authors
  1. Urszula Boryczka
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Editor information

Editors and Affiliations

  1. Institute of Mathematics, Warsaw University of Technology, Pl. Politechniki 1, 00-665, Warszawa, Poland

    Lech Polkowski

  2. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warszawa, Poland

    Andrzej Skowron

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© 1998 Springer-Verlag Berlin Heidelberg

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Boryczka, U. (1998). Learning with Delayed Rewards in Ant Systems for the Job-Shop Scheduling Problem. In: Polkowski, L., Skowron, A. (eds) Rough Sets and Current Trends in Computing. RSCTC 1998. Lecture Notes in Computer Science(), vol 1424. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-69115-4_37

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  • DOI: https://doi.org/10.1007/3-540-69115-4_37

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64655-6

  • Online ISBN: 978-3-540-69115-0

  • eBook Packages: Springer Book Archive

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