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Języki publikacji
Abstrakty
In this paper, an Evolutionary Multi-agent system-based computing process is subjected to a detailed analysis of its parameters in order to establish a base for a better understanding of the meta-heuristics from the practitioner’s point of view. After reviewing the concepts of EMAS and its immunological variant, a series of experiments is shown, and results of the influence of the search outcomes by certain parameters is discussed.
Wydawca
Czasopismo
Rocznik
Tom
Strony
491--512
Opis fizyczny
Bibliogr. 30 poz., rys., wykr., tab.
Twórcy
autor
- AGH University of Science and Technology, Department of Computer Science, Krakow, Poland
Bibliografia
- [1] Bouvry P., Gonzalez-Velez H., Kolodziej J.: Intelligent Decision Systems in Large-Scale Distributed Environments. Springer, 2011.
- [2] Byrski A., Drezewski R., Siwik L., Kisiel-Dorohinicki M.: Evolutinoary multi-agent systems. The Knowledge Engineering Review, 2013 (accepted for printing).
- [3] Byrski A., Kisiel-Dorohinicki M.: Agent-based evolutionary and immunological optimization. In Computational Science – ICCS 2007, 7th International Conference, Beijing, China, May 27–30, 2007, Proceedings. Springer, 2007.
- [4] Byrski A., Kisiel-Dorohinicki M., Nawarecki E.: Agent-based evolution of neural network architecture. In Hamza M., editor, Proc. of the IASTED Int. Symp.: Applied Informatics . IASTED/ACTA Press, 2002.
- [5] Byrski A., Schaefer R., Smolka M.: Asymptotic guarantee of success for multi-agent memetic systems. Bulletin of the Polish Academy of Sciences–Technical Sciences, (1), 2013.
- [6] Byrski A., Debski R., Kisiel-Dorohinicki M.: Agent-based computing in an augmented cloud environment. Computer Systems Science and Engineering, 27(1), 2012.
- [7] Cantu-Paz E.: A summary of research on parallel genetic algorithms. IlliGAL Report No. 95007. University of Illinois, 1995.
- [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., editor, Proc. of the 2nd Int. Conf. on Multi-Agent Systems (ICMAS’96). AAAI Press, 1996.
- [9] Chen S.-H., Kambayashi Y., Sato H.: Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies. IGI Global, 2011.
- [10] Dasgupta D., Nino L.: Immunological Computation Theory and Applications. Auerbach, 2008.
- [11] Digalakis J., Margaritis K.: An experimental study of benchmarking functions for evolutionary algorithms. International Journal of Computer Mathemathics, 79(4):403–416, April 2002.
- [12] Dresner K., Stone P.: A multiagent approach to autonomous intersection management. Journal of Artificial Intelligence Research, 31:591–656, 2008.
- [13] Drezewski R.: Co-evolutionary multi-agent system with speciation and resource sharing mechanisms. Computing and Informatics, 25(4):305–331, 2006.
- [14] Drezewski R., Sepielak J., Siwik L.: Classical and agent-based evolutionary algorithms for investment strategies generation. In Brabazon A., O’Neill M., editors, Natural Computing in Computational Finance, vol. 185 of Studies in Computational Intelligence, pp. 181–205. Springer-Verlag, 2009.
- [15] George J., Gleizes M., P.Glize., C.Regis.: Real-time simulation for flood forecast: an adaptive multi-agent system staff. In Proceedings of the AISB’03 Symposium on Adaptive Agents and Multi-Agent Systems. University of Wales, 2003.
- [16] Jennings N., Faratin P., Johnson M., Norman T., OBrien P., Wiegand M.: Agent-based business process management. International Journal of Cooperative Information Systems, 5(2–3):105–130, 1996.
- [17] Kisiel-Dorohinicki M.: Agent-oriented model of simulated evolution. In Grosky W. I., Plasil F., editors, SofSem 2002: Theory and Practice of Informatics, vol. 2540 of LNCS. Springer-Verlag, 2002.
- [18] Lobel B., Ozdaglar A., Feijer D.: Distributed multi-agent optimization with state-dependent communication. Mathematical Programming, 129(2):255–284, 2011.
- [19] Mahalanobis P.: On the generalised distance in statistics. Proceedings of the National Institute of Sciences of India, 2(1):49–55, 1936.
- [20] McArthur S., Catterson V., Hatziargyriou N.: Multi-agent systems for power engineering applications part I: Concepts, approaches, and technical challenges. IEEE Transactions On Power Systems, 22(4), November 2007.
- [21] Michalewicz Z.: Genetic Algorithms Plus Data Structures Equals Evolution Programs. Springer-Verlag New York, Inc., Secaucus, NJ, USA, 1994.
- [22] Moya L. J., Tolk A.: Towards a taxonomy of agents and multi-agent systems. In Proceedings of the 2007 spring simulation multiconference – Vol. 2, pp. 11–18, San Diego, CA, USA, 2007. Society for Computer Simulation International.
- [23] Sarker R., Ray T.: Agent-Based Evolutionary Search, volume 5 of Adaptation, Learning and Optimization. Springer, 1 edition, 2010.
- [24] Schaefer R., Kolodziej J.: Genetic search reinforced by the population hierarchy. Foundations of Genetic Algorithms, 7, 2003.
- [25] Siwik L., Drezewski R.: Agent-based multi-objective evolutionary algorithms with cultural and immunological mechanisms. In dos Santos W. P., editor, Evolutionary computation, pp. 541–556. In-Teh, 2009.
- [26] Uhruski P., Grochowski M., Schaefer R.: A two-layer agent-based system for large-scale distributed computation. Computational Intelligence, 24(3):191–212, July 2008.
- [27] Wierzchon S.: Function optimization by the immune metaphor. Task Quaterly, 6(3): 1–16, 2002.
- [28] Wooldridge M.: An Introduction to Multiagent Systems. John Wiley & Sons, 2009.
- [29] Wrobel K., Torba P., Paszynski M., Byrski A.: Evolutionary multi-agent computing in inverse problems. Computer Science (accepted for printing), 2013.
- [30] Zhong W., Liu J., Xue M., Jiao L.: A multiagent genetic algorithm for global numerical optimization. IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics , 34(2):1128–1141, 2004.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b34f30ef-9aaa-4ed4-bd14-ad726c82918a