Tuning of agent-based computing

Aleksander Byrski

Abstract


In this paper an Evolutionary Multi-agent system based computing processis subjected to detailed analysis of the parameters in order to ground a basefor better understanding this 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 theresults of influencing of search outcomes by certain parameters are discussed.

Keywords


agent-based computing, agent-based meta-heuristics, biologically-inspired computing

Full Text:

PDF

References


Byrski A., Dreżewski R., Siwik L., Kisiel-Dorohinicki M.:. Evolutinoary multiagent systems. The Knowledge Engineering Review, 2013 (accepted for printing).

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.

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.

Cantu-Paz E.:. A summary of research on parallel genetic algorithms. IlliGAL Report No. 95007. University of Illinois, 1995.

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.

Chen S.-H., Kambayashi Y., Sato H.:. Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies. IGI Global, 2011.

Dasgupta D., Nino L.:. Immunological Computation Theory and Applications. Auerbach, 2008.

Digalakis J., Margaritis K.:. An experimental study of benchmarking functions for evolutionary algorithms. International Journal of Computer Mathemathics,

(4):403–416, April 2002.

Dreżewski R.:. Co-evolutionary multi-agent system with speciation and resource sharing mechanisms. Computing and Informatics, 25(4):305–331, 2006.

Dreżewski 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, volume 185 of Studies in Computational Intelligence, pages 181–205. Springer-Verlag, 2009.

Kisiel-Dorohinicki M.:. Agent-oriented model of simulated evolution. In Grosky W. I., Plasil F., editors, SofSem 2002: Theory and Practice of Informatics, volume 2540 of LNCS. Springer-Verlag, 2002.

Kisiel-Dorohinicki M., Dobrowolski G., Nawarecki E.:. Agent populations as computational intelligence. In Rutkowski L., Kacprzyk J., editors, Neural Networks and Soft Computing, pages 608–614. Physica Verlag, 2002.

Mahalanobis P.:. On the generalised distance in statistics. Proceedings of the National Institute of Sciences of India, 2(1):49–55, 1936.

Sarker R., Ray T.:. Agent-Based Evolutionary Search, volume 5 of Adaptation, Learning and Optimization. Springer, 1 edition, 2010.

Schaefer R., Kołodziej J.:. Genetic search reinforced by the population hierarchy. Foundations of Genetic Algorithms, 7, 2003.

Siwik L., Dreżewski R.:. Agent-based multi-objective evolutionary algorithms with cultural and immunological mechanisms. In dos Santos W. P., editor, Evolutionary computation, pages 541–556. In-Teh, 2009.

Wierzchoń S.:. Function optimization by the immune metaphor. Task Quaterly, 6(3):1–16, 2002.

Wróbel K., Torba P., Paszyński M., Byrski A.:. Evolutionary multi-agent computing in inverse problems. Computer Science (accepted for printing), 2013.

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.




DOI: https://doi.org/10.7494/csci.2013.14.3.491

Refbacks

  • There are currently no refbacks.