Abstract
This paper investigates the behaviour of the Evolvable Agent model (EvAg) in static and dynamic environments. The EvAg is a spatially structured Genetic Algorithm (GA) designed to work on Peer-to-Peer (P2P) systems in which the population structure is a small-world graph built by newscast, a P2P protocol. Additionally to the profits in computing performance, EvAg maintains genetic diversity at the small-world relationships between individuals in a sort of social network. Experiments were conducted in order to assess how EvAg scales on deceptive and non-deceptive trap functions. In addition, the proposal was tested on dynamic environments. The results show that the EvAg scales and adapts better to dynamic environments than a standard GA and an improved version of the well-known Random Immigrants Genetic Algorithm.
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
Ackley, D.H.: A connectionist machine for genetic hillclimbing. Kluwer Academic Publishers, Norwell (1987)
Alba, E., Badia, J.S., Luque, G.: A Study of Canonical GAs for NSOPs. In: Meteheuristics. Operations Research/Computer Science Interfaces, vol. 39, pp. 245–260. Springer, US (2007)
Angeline, P.J.: Tracking extrema in dynamic environments. In: Angeline, P.J., McDonnell, J.R., Reynolds, R.G., Eberhart, R. (eds.) EP 1997. LNCS, vol. 1213. Springer, Heidelberg (1997)
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)
Giacobini, M., Tomassini, M., Tettamanzi, A., Alba, E.: Selection intensity in cellular evolutionary algorithms for regular lattices. IEEE Transactions on Evolutionary Computation 9(5), 489–505 (2005)
Giacobini, M., Alba, E., Tettamanzi, A., Tomassini, M.: Modeling selection intensity for toroidal cellular evolutionary algorithms. In: EWSPT 1996. LNCS, vol. 1149, pp. 1138–1149. Springer, Heidelberg (1996)
Giacobini, M., Preuss, M., Tomassini, M.: Effects of scale-free and small-world topologies on binary coded self-adaptive CEA. In: Gottlieb, J., Raidl, G.R. (eds.) EvoCOP 2006. LNCS, vol. 3906, pp. 85–96. Springer, Heidelberg (2006)
Jelasity, M., van Steen, M.: Large-scale newscast computing on the Internet. Technical Report IR-503, Vrije Universiteit Amsterdam, Department of Computer Science, Amsterdam, The Netherlands (October 2002)
Laredo, J.L.J., Eiben, E.A., Schoenauer, M., Castillo, P.A., Mora, A.M., Merelo, J.J.: Exploring selection mechanisms for an agent-based distributed evolutionary algorithm. In: GECCO 2007, pp. 2801–2808. ACM Press, New York (2007)
Laredo, J.L.J., Castillo, P.A., Mora, A.M., Merelo, J.J.: Exploring population structures for locally concurrent and massively parallel evolutionary algorithms. In: IEEE Congress on Evolutionary Computation (CEC2008), WCCI 2008 Proceedings, June 2008, pp. 2610–2617. IEEE Computer Society Press, Los Alamitos (2008)
Preuss, M., Lasarczyk, C.: On the importance of information speed in structured populations. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 91–100. Springer, Heidelberg (2004)
Sarma, J., De Jong, K.A.: The behavior of spatially distributed evolutionary algorithms in non-stationary environments. In: Banzhaf, W., Daida, J.M., Eiben, E.A., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E. (eds.) GECCO 1999, Orlando, FL, USA, pp. 572–578. Morgan Kaufmann, San Francisco (1999)
Sastry, K.: Evaluation-relaxation schemes for genetic and evolutionary algorithms. Technical Report 2002004, University of Illinois at Urbana-Champaign, Urbana, IL (2001)
Tinós, R., Yang, S.: A self-organizing random immigrants genetic algorithm for dynamic optimization problems. Genetic Programming and Evolvable Machines 8(3), 255–286 (2007)
Tomassini, M.: Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time. Natural Computing Series. Springer, New York (2005)
Voulgaris, S., Jelasity, M., van Steen, M.: A Robust and Scalable Peer-to-Peer Gossiping Protocol. In: Moro, G., Sartori, C., Singh, M.P. (eds.) AP2PC 2003. LNCS (LNAI), vol. 2872, pp. 47–58. Springer, Heidelberg (2004)
Yang, S., Yao, X.: Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Comput. 9(11), 815–834 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Laredo, J.L.J., Castillo, P.A., Mora, A.M., Merelo, J.J., Rosa, A., Fernandes, C. (2008). Evolvable Agents in Static and Dynamic Optimization Problems. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds) Parallel Problem Solving from Nature – PPSN X. PPSN 2008. Lecture Notes in Computer Science, vol 5199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87700-4_49
Download citation
DOI: https://doi.org/10.1007/978-3-540-87700-4_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87699-1
Online ISBN: 978-3-540-87700-4
eBook Packages: Computer ScienceComputer Science (R0)