Abstract
We introduce a novel evolutionary algorithm where the centralized oracle –the selection-reproduction loop– is replaced by a distributed system of Fate Agents that autonomously perform the evolutionary operations. This results in a distributed, situated, and self-organizing EA, where candidate solutions and Fate Agents co-exist and co-evolve. Our motivation comes from evolutionary swarm robotics where candidate solutions evolve in real time and space. As a first proof-of-concept, however, here we test the algorithm with abstract function optimization problems. The results show that the Fate Agents EA is capable of evolving good solutions and it can cope with noise and changing fitness landscapes. Furthermore, an analysis of algorithm behavior also shows that this EA successfully regulates population sizes and adapts its parameters.
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
Alba, E., Dorronsoro, B.: Cellular Genetic Algorithms. Springer (2008)
Eiben, A., Michalewicz, Z., Schoenauer, M., Smith, J.: Parameter control in evolutionary algorithms. In: Lobo, et al. [8], pp. 19–46
Freisleben, B.: Meta-evolutionary approaches. In: Bäck, T., Fogel, D., Michalewicz, Z. (eds.) Handbook of Evolutionary Computation, pp. 214–223. Institute of Physics Publishing, Oxford University Press, Bristol, New York (1997)
Gong, Y., Fukunaga, A.: Distributed island-model genetic algorithms using heterogeneous parameter settings. In: IEEE Congress on Evolutionary Computation, pp. 820–827 (2011)
Gordon, V.S., Pirie, R., Wachter, A., Sharp, S.: Terrain-based genetic algorithm (TBGA): Modeling parameter space as terrain. In: Banzhaf, W., Daida, J., Eiben, A., Garzon, M., Honavar, V., Jakiela, M., Smith, R. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 1999, pp. 229–235. Morgan Kaufmann, San Francisco (1999)
Kantschik, W., Dittrich, P., Brameier, M., Banzhaf, W.: Meta-Evolution in Graph GP. In: Langdon, W.B., Fogarty, T.C., Nordin, P., Poli, R. (eds.) EuroGP 1999. LNCS, vol. 1598, pp. 15–28. Springer, Heidelberg (1999)
Karafotias, G., Haasdijk, E., Eiben, A.: An algorithm for distributed on-line, on-board evolutionary robotics. In: Krasnogor, N., Lanzi, P.L., Engelbrecht, A., Pelta, D., Gershenson, C., Squillero, G., Freitas, A., Ritchie, M., Preuss, M., Gagne, C., Ong, Y.S., Raidl, G., Gallager, M., Lozano, J., Coello-Coello, C., Silva, D.L., Hansen, N., Meyer-Nieberg, S., Smith, J., Eiben, G., Bernado-Mansilla, E., Browne, W., Spector, L., Yu, T., Clune, J., Hornby, G., Wong, M.-L., Collet, P., Gustafson, S., Watson, J.-P., Sipper, M., Poulding, S., Ochoa, G., Schoenauer, M., Witt, C., Auger, A. (eds.) Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011, Dublin, Ireland, July 12-16, pp. 171–178. ACM (2011)
Lobo, F., Lima, C., Michalewicz, Z. (eds.): Parameter Setting in Evolutionary Algorithms. Springer (2007)
Nellis, A.: Meta evolution. Qualifying Dissertation (2009)
Samsonovich, A.V., De Jong, K.A.: Pricing the ’free lunch’ of meta-evolution. In: Beyer, H.-G., O’Reilly, U.-M. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2005, pp. 1355–1362. ACM (2005)
Schut, M., Haasdijk, E., Eiben, A.E.: What is situated evolution? In: Proceedings of the 2009 IEEE Congress on Evolutionary Computation, Trondheim, May 18-21, pp. 3277–3284. IEEE Press (2009)
Tomassini, M.: Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time. Natural Computing Series. Springer-Verlag New York, Inc., Secaucus (2005)
Watson, R.A., Ficici, S.G., Pollack, J.B.: Embodied evolution: Distributing an evolutionary algorithm in a population of robots. Robotics and Autonomous Systems 39(1), 1–18 (2002)
Wickramasinghe, W., van Steen, M., Eiben, A.E.: Peer-to-peer evolutionary algorithms with adaptive autonomous selection. In: D.T., et al. (eds.) Proc of the 9th conference on Genetic and Evolutionary Computation, GECCO 2007, pp. 1460–1467. ACM Press (2007)
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
Bim, J., Karafotias, G., Smit, S.K., Eiben, A.E., Haasdijk, E. (2012). It’s Fate: A Self-organising Evolutionary Algorithm. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds) Parallel Problem Solving from Nature - PPSN XII. PPSN 2012. Lecture Notes in Computer Science, vol 7492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32964-7_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-32964-7_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32963-0
Online ISBN: 978-3-642-32964-7
eBook Packages: Computer ScienceComputer Science (R0)