Abstract
A stationary Markov chain model of the agent-based computation system EMAS is presented. The primary goal of the model is better understanding the behavior of this class of systems as well as their constraints. The ergodicity of this chain can be verified for the particular case of EMAS, thus implying an asymptotic guarantee of success (the ability of finding all solutions of the global optimization problem). The presented model may be further adapted to numerous evolutionary and memetic systems.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Berretta, R., Cotta, C., Moscato, P.: Enhancing the performance of memetic algorithms by using a matching-based recombination algorithm: Results on the number partitioning problem. In: Resende, M., Pinho de Sousa, J. (eds.) Metaheuristics: Computer-Decision Making, pp. 65–90. Kluwer Academic Publishers, Boston (2003)
Berretta, R., Moscato, P.: The number partitioning problem: An open challenge for evolutionary computation ? In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 261–278. McGraw-Hill, New York (1999)
Byrski, A., Kisiel-Dorohinicki, M.: Agent-based evolutionary and immunological optimization. In: Proceedings of Computational Science - ICCS 2007, 7th International Conference, Beijing, China, May 27-30. Springer, Heidelberg (2007)
Byrski, A., Schaefer, R.: Stochastic model of evolutionary and immunological multi-agent systems: Mutually exclusive actions. Fundamenta Informaticae 95(2-3), 263–285 (2009)
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.) ICMAS 1996 Proceedings. AAAI Press, Menlo Park (1996)
Chira, C., Gog, A., Dumitrescu, D.: Exploring population geometry and multi-agent systems: a new approach to developing evolutionary techniques. In: Proceedings of the 2008 GECCO Conference Companion on Genetic and Evolutionary Computation, Atlanta, GA, pp. 1953–1960. ACM Press, New York (2008)
Hart, W.E., Krasnogor, N., Smith, J.E.: Memetic evolutionary algorithms. In: Recent Advances in Memetic Algorithms. Studies in Fuzziness and Soft Computing, vol. 166, pp. 3–27. Springer, Heidelberg (2005)
Horst, R., Pardalos, P.: Handbook of Global Optimization. Kluwer, Dordrecht (1995)
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)
Krasnogor, N., Gustafson, S.: A study on the use of “self-generation” in memetic algorithms. Natural Computing 3, 53–76 (2004)
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)
Michalewicz, Z.: Genetic Algorithms Plus Data Structures Equals Evolution Programs. Springer, New York (1994)
Moscato, P.: On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms. Technical Report Caltech Concurrent Computation Program, Report. 826, California Institute of Technology, Pasadena, California, USA (1989)
Moscato, P.: Memetic algorithms: A short introduction. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 219–234. McGraw-Hill, New York (1999)
Moscato, P., Cotta, C.: A gentle introduction to memetic algorithms. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, pp. 105–144. Kluwer Academic Publishers, Boston (2003)
Norman, M.G., Moscato, P.: A competitive and cooperative approach to complex combinatorial search. Technical Report Caltech Concurrent Computation Program, Report. 790, California Institute of Technology, Pasadena, California, USA (1989)
Radcliffe, N.J., Surry, P.D.: Formal Memetic Algorithms. In: Fogarty, T.C. (ed.) AISB-WS 1994. LNCS, vol. 865, pp. 1–16. Springer, Heidelberg (1994)
Rinnoy Kan, A., Timmer, G.: Stochastic global optimization methods. Mathematical Programming 39, 27–56 (1987)
Rudolph, G.: Models of stochastic convergence. In: Bäck, T., Fogel, D.B., Michalewicz, Z. (eds.) Handbook of Evolutionary Computations. Oxford University Press, Oxford (1997)
Rudolph, G.: Stochastic processes. In: Bäck, T., Fogel, D.B., Michalewicz, Z. (eds.) Handbook of Evolutionary Computations. Oxford University Press, Oxford (1997)
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)
Barkat Ullah, A.S.S.M., Sarker, R., Lokan, C.: An agent-based memetic algorithm (AMA) for nonlinear optimization with equality constraints. In: Proceedings of the Eleventh Conference on Congress on Evolutionary Computation, Trondheim, Norway, pp. 70–77. IEEE Press, Los Alamitos (2009)
Vose, M.: The Simple Genetic Algorithm: Foundations and Theory. MIT Press, Cambridge (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Byrski, A., Schaefer, R., Smołka, M., Cotta, C. (2010). Asymptotic Analysis of Computational Multi-Agent Systems. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15844-5_48
Download citation
DOI: https://doi.org/10.1007/978-3-642-15844-5_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15843-8
Online ISBN: 978-3-642-15844-5
eBook Packages: Computer ScienceComputer Science (R0)