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Man-Machine Interactions 3 pp 487–494Cite as

Agent-Based Approach to Continuous Optimisation

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 242))

Abstract

In the paper an application of selected agent-based evolutionary computing systems, such as flock-based multi agent system (FLOCK) and evolutionary multi-agent system (EMAS), to the problem of continuous optimisation is presented. Hybridising of agent-based paradigm with evolutionary computation brings a new quality to the meta-heuristic field, easily enhancing individuals with possibilities of perception, interaction with other individuals (agents), adaptation of the search parameters, etc. The experimental examination of selected benchmarks allows to gather the observation regarding the overall efficiency of the systems in comparison to the classical genetic algorithm(as defined by Michalewicz) and memetic versions of all the systems.

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References

  1. Bäck, T., Fogel, D.B., Michalewicz, Z. (eds.): Handbook of Evolutionary Computation. IOP Publishing and Oxford University Press (1997)

    Google Scholar 

  2. Byrski, A., Dreżewski, R., Siwik, L., Kisiel-Dorohinicki, M.: Evolutionary multi-agent systems. The Knowledge Engineering Review (2012)

    Google Scholar 

  3. Byrski, A., Kisiel-Dorohinicki, M.: Evolving RBF networks in a multi-agent system. Neural Network World 12(2), 433–440 (2002)

    Google Scholar 

  4. Byrski, A., Kisiel-Dorohinicki, M.: Immune-based optimization of predicting neural networks. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2005. LNCS, vol. 3516, pp. 703–710. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  5. Byrski, A., Kisiel-Dorohinicki, M.: Agent-based model and computing environment facilitating the development of distributed computational intelligence systems. In: Allen, G., Nabrzyski, J., Seidel, E., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2009, Part II. LNCS, vol. 5545, pp. 865–874. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  6. Byrski, A., Schaefer, R.: Stochastic model of evolutionary and immunological multi-agent systems: Mutually exclusive actions. Fundamenta Informaticae 95(2-3), 263–285 (2009)

    MathSciNet  MATH  Google Scholar 

  7. Cantú-Paz, E.: A summary of research on parallel genetic algorithms. Tech. Rep. IlliGAL Report No. 95007, Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign (1995)

    Google Scholar 

  8. Cetnarowicz, K., Kisiel-Dorohinicki, M., Nawarecki, E.: The application of evolution process in Multi-Agent World (MAW) to the prediction system. In: Tokoro, M. (ed.) Proceedings of the 2nd International Conference on Multi-Agent Systems (ICMAS 1996). AAAI Press (1996)

    Google Scholar 

  9. Dawkins, R.: The Selfish Gene: 30th Anniversary edition. Oxford University Press, New York City (2006)

    Google Scholar 

  10. Digalakis, J.G., Margaritis, K.G.: An experimental study of benchmarking functions for evolutionary algorithms. International Journal of Computer Mathemathics 79(4), 403–416 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  11. Hiroyasu, T., Miki, M., Hamasaki, M., Tanimura, Y.: A new model of parallel distributed genetic algorithms for cluster systems: Dual individual DGAs. In: Valero, M., Joe, K., Kitsuregawa, M., Tanaka, H. (eds.) ISHPC 2000. LNCS, vol. 1940, pp. 374–383. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  12. Kisiel-Dorohinicki, M.: Agent-oriented model of simulated evolution. In: Grosky, W.I., Plášil, F. (eds.) SOFSEM 2002. LNCS, vol. 2540, pp. 253–261. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  13. Kisiel-Dorohinicki, M.: Flock-based architecture for distributed evolutionary algorithms. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 841–846. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  14. Kisiel-Dorohinicki, M., Dobrowolski, G., Nawarecki, E.: Agent populations as computational intelligence. In: Rutkowski, L., Kacprzyk, J. (eds.) Proceedings of 6th International Conference on Neural Networks and Soft Computing (ICNNSC 2002). Advances in Soft Computing, vol. 19, pp. 608–613. Physica-Verlag (2003)

    Google Scholar 

  15. Krasnogor, N., Smith, J.: A tutorial for competent memetic algorithms: Model, taxonomy, and design issues. IEEE Transactions on Evolutionary Computation 9(5), 474–488 (2005)

    Article  Google Scholar 

  16. Michalewicz, Z.: Genetic Algorithms Plus Data Structures Equals Evolution Programs, 2nd edn. Springer (1994)

    Google Scholar 

  17. Moscato, P.: Memetic algorithms: a short introduction. In: Corne, D., Dorigo, M., Glover, F., Dasgupta, D., Moscato, P., Poli, R., Price, K.V. (eds.) New Ideas in Optimization, pp. 219–234. McGraw-Hill (1999)

    Google Scholar 

  18. Moscato, P., Cotta, C.: A modern introduction to memetic algorithms. In: Gendreau, M., Potvin, J.Y. (eds.) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol. 146, pp. 141–183. Springer, US (2010)

    Chapter  Google Scholar 

  19. Ong, Y.S., Lim, M.H., Che, X.: Memetic computation – past, present & future. IEEE Computational Intelligence Magazine 5(2), 24–31 (2010)

    Article  Google Scholar 

  20. Schaefer, R., Byrski, A., Smołka, M.: Stochastic model of evolutionary and immunological multi-agent systems: Parallel execution of local actions. Fundamenta Informaticae 95(2-3), 325–348 (2009)

    MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059, Krakow, Poland

    Aleksander Byrski & Marek Kisiel-Dorohinicki

Authors
  1. Aleksander Byrski
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  2. Marek Kisiel-Dorohinicki
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Corresponding author

Correspondence to Aleksander Byrski .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Dr. Aleksandra Gruca

  2. Polish Academy of Sciences and Silesian University of Technology, Gliwice, Poland

    Tadeusz Czachórski

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

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Byrski, A., Kisiel-Dorohinicki, M. (2014). Agent-Based Approach to Continuous Optimisation. In: Gruca, D., Czachórski, T., Kozielski, S. (eds) Man-Machine Interactions 3. Advances in Intelligent Systems and Computing, vol 242. Springer, Cham. https://doi.org/10.1007/978-3-319-02309-0_53

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  • DOI: https://doi.org/10.1007/978-3-319-02309-0_53

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02308-3

  • Online ISBN: 978-3-319-02309-0

  • eBook Packages: EngineeringEngineering (R0)

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