Abstract
We want to answer the question how effectively finite state machines (FSMs) controlling the behavior of local agents in a multi agent system with a global task can be evolved by a genetic algorithm (GA). Different variants of the GA were used by varying the mutation techniques and the population size. In order to evaluate the effectiveness of the GA the optimal behavior is used for comparison. This optimal behavior can be found by a sophisticated enumeration technique. The agents’ global task is to explore an unknown area with obstacles. The number of states of the controlling FSM was restricted to five in order to keep the computation time for the enumeration acceptable. The results show that the GA is reliable and almost as effective as enumeration while being significantly faster.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Halbach, M., Heenes, W., Hoffmann, R., Tisje, J.: Optimizing the behavior of a moving creature in software and in hardware. Springer, Heidelberg (2004)
Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. The MIT Press, Cambridge (1994)
Halbach, M.: Algorithmen und Hardwarearchitekturen zur optimierten Aufzählung von Automaten und deren Einsatz bei der Simulation künstlicher Kreaturen. PhD thesis, Technische Universität Darmstadt (2008)
Komann, M., Ediger, P., Fey, D., Hoffmann, R.: On the Effectivity of Genetic Programming Compared to the Time-Consuming Full Search of Optimal 6-State Automata. In: Vanneschi, L., Gustafson, S., Moraglio, A., De Falco, I., Ebner, M. (eds.) EuroGP 2009. LNCS, vol. 5481, pp. 280–291. Springer, Heidelberg (2009)
Ediger, P., Hoffmann, R., Halbach, M.: Evolving 6-state Automata for Optimal Behaviors of Creatures Compared to Exhaustive Search. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2009. LNCS, vol. 5717, pp. 689–696. Springer, Heidelberg (2009)
Komann, M., Fey, D.: Evaluating the Evolvability of Emergent Agents with Different Numbers of States
Di Stefano, B.N., Lawniczak, A.T.: Autonomous roving object’s coverage of its universe. In: CCECE, pp. 1591–1594. IEEE, Los Alamitos (2006)
Halbach, M., Hoffmann, R., Both, L.: Optimal 6-state algorithms for the behavior of several moving creatures. In: El Yacoubi, S., Chopard, B., Bandini, S. (eds.) ACRI 2006. LNCS, vol. 4173, pp. 571–581. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ediger, P., Hoffmann, R., Grüner, S. (2012). Effectively Evolving Finite State Machines Compared to Enumeration. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2011. EUROCAST 2011. Lecture Notes in Computer Science, vol 6927. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27549-4_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-27549-4_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27548-7
Online ISBN: 978-3-642-27549-4
eBook Packages: Computer ScienceComputer Science (R0)