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Fine tuning of agent-based evolutionary computing

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Evolutionary Multi-agent System introduced by late Krzysztof Cetnarowicz and developed further at the AGH University of Science and Technology became a reliable optimization system, both proven experimentally and theoretically. This paper follows a work of Byrski further testing and analyzing the efficacy of this metaheuristic based on popular, high-dimensional benchmark functions. The contents of this paper will be useful for anybody willing to apply this computing algorithm to continuous and not only optimization.
Rocznik
Strony
81--97
Opis fizyczny
Bibliogr. 33 poz., rys.
Twórcy
  • Department of Computer Science, Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland
  • Department of Computer Science, Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland
  • Department of Computer Science, Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland
  • Department of Computer Science, Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland
Bibliografia
  • [1] P. Adamidis. Parallel evolutionary algorithms: A review. In Proceedings of the 4th Hellenic-European Conference on Computer Mathematics and its Applications (HERCMA 1998), Athens, Greece, 1998.
  • [2] T. Back and H.-P. Schwefel. Evolutionary computation: An overview. In T. Fukuda and T. Furuhashi, editors, Proceedings of the Third IEEE Conference on Evolutionary Computation. IEEE Press, 1996.
  • [3] A. Byrski, M. Kisiel-Dorohinicki, and E. Nawarecki. Agent-based evolution of neural network architecture. In M. Hamza, editor, Proc. of the IASTED Int. Symp.: Applied Informatics. IASTED/ACTA Press, 2002.
  • [4] A. Byrski, M. Kisiel-Dorohinicki, and N. Tusinski. Extending estimation of distribution algorithms with gent-based computing inspirations. Transactions on Computational Collective Intelligence, XXVII, 2017.
  • [5] A. Byrski, R. Schaefer, M. Smołka, and C. Cotta.Asymptotic guarantee of success for multi-agent memetic systems. Bulletin of the Polish Academy of Sciences – Technical Sciences, 61(1), 2013.
  • [6] Aleksander Byrski. Tuning of agent-based computing. Computer Science, 14(3):491, 2013.
  • [7] Aleksander Byrski. Tuning of agent-based computing. Computer Science (accepted), 2013.
  • [8] Aleksander Byrski, Roman Debski, and Marek Kisiel-Dorohinicki. Agent-based computing in an augmented cloud environment. Computer Systems Science and Engineering, 27(1), 2012.
  • [9] Aleksander Byrski, Rafal Drezewski, Leszek Siwik, and Marek Kisiel-Dorohinicki. Evolutionary multiagent systems. Knowledge Eng. Review, 30(2):171–186, 2015.
  • [10] Aleksander Byrski and Marek Kisiel-Dorohinicki. Immune-based optimization of predicting neural networks. In Vaidy S. Sunderam, Geert Dick van Albada, Peter M. A. Sloot, and Jack Dongarra, editors, Computational Science – ICCS 2005, pages 703–710, Berlin, Heidelberg, 2005. Springer Berlin Heidelberg.
  • [11] Aleksander Byrski and Marek Kisiel-Dorohinicki. Agent-based evolutionary and immunological optimization. In Yong Shi, Geert Dick van Albada, Jack Dongarra, and Peter M. A. Sloot, editors, Computational Science – ICCS 2007, pages 928–935, Berlin, Heidelberg, 2007. Springer Berlin Heidelberg.
  • [12] Aleksander Byrski and Marek Kisiel-Dorohinicki. Agent-based model and computing environment facilitating the development of distributed computational intelligence systems. In Gabrielle Allen, Jarosław Nabrzyski, Edward Seidel, Geert Dick van Albada, Jack Dongarra, and Peter M. A. Sloot, editors, Computational Science – ICCS 2009, pages 865–874, Berlin, Heidelberg, 2009. Springer Berlin Heidelberg.
  • [13] Aleksander Byrski and Marek Kisiel-Dorohinicki. Evolutionary Multi-agent Systems: From inspirations to applications, volume 680 of Studies in Computational Intelligence. Springer, 2017.
  • [14] E. Cantu-Paz. A summary of research on parallel genetic algorithms. IlliGAL Report No. 95007. University of Illinois, 1995.
  • [15] E. Cantu-Paz. A survey of parallel genetic algorithms. Calculateurs Paralleles, Reseaux et Systems Repartis, 10(2):141–171, 1998.
  • [16] K. Cetnarowicz, M. Kisiel-Dorohinicki, and E. Nawarecki. The application of evolution proces in multi-agent world (MAW) to the prediction system. In M. Tokoro, editor, Proc. of the 2nd Int. Conf. on Multi-Agent Systems (ICMAS’96). AAAI Press, 1996.
  • [17] J. Digalakis and K. Margaritis. An experimental study of benchmarking functions for evolutionary algorithms. International Journal of Computer Mathemathics, 79(4):403–416, April 2002.
  • [18] Rafał Drezewski. Co-evolutionary multi-agent system with speciation and resource sharing mechanisms. Computing and Informatics, 25(4):305–331, 2006.
  • [19] Rafał Drezewski, Jan Sepielak, and Leszek Siwik. Classical and agent-based evolutionary algorithms for investment strategies generation. In Anthony Brabazon and Michael O’Neill, editors, Natural Computing in Computational Finance, volume 185 of Studies in Computational Intelligence, pages 181–205. Springer-Verlag, 2009.
  • [20] Stan Franklin and Art Graesser. Is it an agent, or just a program?: A taxonomy for autonomous agents. In Proceedings of the Workshop on Intelligent Agents III, Agent Theories, Architectures, and Languages, ECAI ’96, pages 21–35, London, UK, UK, 1997. Springer-Verlag.
  • [21] D. E. Goldberg and J. Richardson. Genetic algorithms with sharing for multimodal function optimization. In J. J. Grefenstette, editor, Proceedings of the 2nd International Conference on Genetic Algorithms, pages 41–49. Lawrence Erlbaum Associates, 1987.
  • [22] L. Hanna and J. Cagan. Evolutionary multi-agent systems: An adaptive and dynamic approach to optimization. ASME Journal of Mechanical Design, 131(1), 2009.
  • [23] M. Kisiel-Dorohinicki. Agent-oriented model of simulated evolution. In William I. Grosky and Frantisek Plasil, editors, SofSem 2002: Theory and Practice of Informatics, volume 2540 of LNCS. Springer-Verlag, 2002.
  • [24] Marek Kisiel-Dorohinicki. Agent-based models and platforms for parallel evolutionary algorithms. In Marian Bubak, Geert Dick van Albada, Peter M. A. Sloot, and Jack Dongarra, editors, Computational Science - ICCS 2004, pages 646–653, Berlin, Heidelberg, 2004. Springer Berlin Heidelberg.
  • [25] Wojciech Korczynski, Aleksander Byrski, and Marek Kisiel-Dorohinicki. Buffered local search for efficient memetic agent-based continuous optimization. Journal of Computational Science, 20:112 –117, 2017.
  • [26] L. Placzkiewicz, M. Sendera, A. Szlachta, M. Paciorek, A. Byrski, M. Kisiel-Dorohinicki, and . Godzik. Hybrid swarm and agent-based evolutionary optimization. In Proc. of International Conference on Computational Science, Wuxi, China (accepted). 2018.
  • [27] Leszek Siwik and Rafał Drezewski. Agent-based ˙ multi-objective evolutionary algorithms with cultural and immunological mechanisms. In Wellington Pinheiro dos Santos, editor, Evolutionary computation, pages 541–556. In-Teh, 2009.
  • [28] Kenneth Sorensen. Metaheuristicsthe metaphor exposed. International Transactions in Operational Research, 22(1):3–18, 2015.
  • [29] Jan Stypka, Wojciech Turek, Aleksander Byrski, Marek Kisiel-Dorohinicki, Adam D. Barwell, Christopher Brown, Kevin Hammond, and Vladimir Janjic. The missing link! a new skeleton for evolutionary multi-agent systems in erlang. International Journal of Parallel Programming, 46(1):4–22, Feb 2018.
  • [30] Wojciech Turek, Jan Stypka, Daniel Krzywicki, Piotr Anielski, Kamil Pietak, Aleksander Byrski, and Marek Kisiel-Dorohinicki. Highly scalable erlang framework for agent-based metaheuristic computing. J. Comput. Science, 17:234–248, 2016.
  • [31] D.H. Wolpert and W.G. Macready. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 67(1), 1997.
  • [32] M.J. Wooldridge. An Introduction to Multiagent Systems. John Wiley & Sons, 2009.
  • [33] Weicai Zhong, Jing Liu, Mingzhi Xue, and Licheng Jiao. A multiagent genetic algorithm for global numerical optimization. IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics, 34(2):1128–1141, 2004.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-91393b08-e7d4-4017-812f-7bfe4bf4dac6
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