Abstract
Aggregate fitness selection is known to suffer from the bootstrap problem, which is often viewed as the main inhibitor of the widespread application of aggregate fitness selection in evolutionary robotics. There remains a need to identify methods that overcome it, while requiring the minimum amount of a priori task knowledge from the designer.
We suggest a novel two-phase method. In the first phase, it exploits multi objective optimization to develop a population of controllers that exhibit several desirable behaviors. In the second phase, it applies aggregate selection using the previously obtained population as the seed. The method is assessed by two non-traditional comparison procedures. The proposed approach is demonstrated using simulated coevolution of two robotic soccer players. The multi objective phase is based on adaptation of the well-known NSGA-II algorithm for coevolution. The results demonstrate the potential advantage of the suggested two-phase approach over the conventional one.
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.
Similar content being viewed by others
References
Nelson, A.L., Barlow, G.J., Doitsidis, L.: Fitness functions in evolutionary robotics: A survey and analysis. Robotics and Autonomous Systems 57, 345–370 (2009)
Mouret, J.B., Doncieux, S.: Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity. In: 2009 IEEE Congress on Evolutionary Computation, CEC 2009, pp. 1161–1168 (2009)
Nelson, A.L., Grant, E.: Using direct competition to select for competent controllers in evolutionary robotics. Robotics and Autonomous Systems 54, 840–857 (2006)
Mouret, J.B., Doncieux, S.: Incremental evolution of animats’ behaviors as a multi-objective optimization. From Animals to Animats 10, 210–219 (2008)
Nolfi, S.: Evolutionary robotics: Exploiting the full power of self-organization. Connect. Sci. 10, 167–184 (1998)
Knowles, J.D., Watson, R.A., Corne, D.W.: Reducing Local Optima in Single-Objective Problems by Multi-objectivization. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 269–283. Springer, Heidelberg (2001)
Moshaiov, A., Ashram, A.: Multi-objective evolution of robot neuro-controllers. In: 2009 IEEE Congress on Evolutionary Computation, CEC 2009, pp. 1093–1100 (2009)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)
Østergaard, E.H., Hautop Lund, H.: Co-evolving Complex Robot Behavior. In: Tyrrell, A.M., Haddow, P.C., Torresen, J. (eds.) ICES 2003. LNCS, vol. 2606, pp. 308–319. Springer, Heidelberg (2003)
Durillo, J.J., Nebro, A.J., Alba, E.: The jmetal framework for multi-objective optimization: Design and architecture. In: 2010 IEEE Congress on Evolutionary Computation, CEC 2010, pp. 1–8 (2010)
Nolfi, S., Floreano, D.: Coevolving Predator and Prey Robots: Do “Arms Races” Arise in Artificial Evolution? Artif. Life 4, 311–335 (1998)
Stanley, K.O., Miikkulainen, R.: The dominance tournament method of monitoring progress in coevolution. In: GECCO 2002, pp. 242–248 (2002)
Miconi, T.: Why Coevolution Doesn’t “Work”: Superiority and Progress in Coevolution. In: Vanneschi, L., Gustafson, S., Moraglio, A., De Falco, I., Ebner, M. (eds.) EuroGP 2009. LNCS, vol. 5481, pp. 49–60. Springer, Heidelberg (2009)
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
Israel, S., Moshaiov, A. (2012). Bootstrapping Aggregate Fitness Selection with Evolutionary Multi-Objective Optimization. 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_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-32964-7_6
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)