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Extending Estimation of Distribution Algorithms with Agent-Based Computing Inspirations

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Book cover Transactions on Computational Collective Intelligence XXVII

Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 10480))

Abstract

In the paper several extensions of a successful EDA-type algorithm, namely \(COMMA_{op}\), inspired by the paradigm of agent-based computing (EMAS) are presented. The proposed algorithms leveraging notions connected with EMAS, such as reproduction and death, or even the population decomposition, turn out to be better than the original algorithm. The evidence for this is presented in the end of the paper, utilizing QAP problems by Éric Taillard as benchmarks.

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Notes

  1. 1.

    http://mistic.heig-vd.ch/taillard/problemes.dir/qap.dir/qap.html.

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Acknowledgment

This research was supported by AGH University of Science and Technology Statutory Fund.

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Authors and Affiliations

  1. Department of Computer Science, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059, Krakow, Poland

    Aleksander Byrski, Marek Kisiel-Dorohinicki & Norbert Tusiński

Authors
  1. Aleksander Byrski
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  2. Marek Kisiel-Dorohinicki
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  3. Norbert Tusiński
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Correspondence to Aleksander Byrski .

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Editors and Affiliations

  1. Department of Finance and Management, The WSB University, Wroclaw, Poland

    Jacek Mercik

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Byrski, A., Kisiel-Dorohinicki, M., Tusiński, N. (2017). Extending Estimation of Distribution Algorithms with Agent-Based Computing Inspirations. In: Mercik, J. (eds) Transactions on Computational Collective Intelligence XXVII. Lecture Notes in Computer Science(), vol 10480. Springer, Cham. https://doi.org/10.1007/978-3-319-70647-4_13

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  • DOI: https://doi.org/10.1007/978-3-319-70647-4_13

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