Abstract
We present an analysis of the behaviour of Cooperative Co-evolution algorithms (CCEAs) on a simple test problem, that is the optimal placement of a set of lamps in a square room, for various problems sizes. Cooperative Co-evolution makes it possible to exploit more efficiently the artificial Darwinism scheme, as soon as it is possible to turn the optimisation problem into a co-evolution of interdependent sub-parts of the searched solution. We show here how two cooperative strategies, Group Evolution (GE) and Parisian Evolution (PE) can be built for the lamps problem. An experimental analysis then compares a classical evolution to GE and PE, and analyses their behaviour with respect to scale.
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References
Amaya, J.E., Cotta, C., Leiva, A.J.F.: A memetic cooperative optimization schema and its application to the tool switching problem. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6238, pp. 445–454. Springer, Heidelberg (2010)
Barrire, O., Lutton, E., Wuillemin, P.H.: Bayesian network structure learning using cooperative coevolution. In: Genetic and Evolutionary Computation Conference, GECCO 2009 (2009)
Bongard, J., Lipson, H.: Active coevolutionary learning of deterministic finite automata. Journal of Machine Learning Research 6, 1651–1678 (2005)
Boumaza, A.M., Louchet, J.: Dynamic flies: Using real-time parisian evolution in robotics. In: Boers, E.J.W., Gottlieb, J., Lanzi, P.L., Smith, R.E., Cagnoni, S., Hart, E., Raidl, G.R., Tijink, H. (eds.) EvoIASP 2001, EvoWorkshops 2001, EvoFlight 2001, EvoSTIM 2001, EvoCOP 2001, and EvoLearn 2001. LNCS, vol. 2037, pp. 288–297. Springer, Heidelberg (2001)
Bucci, A., Pollacj, J.B.: On identifying global optima in cooperative coevolution. In: Proceedings of the 2005 Conference on Genetic and Evolutionary, Washington DC, USA (2005)
Chen, W., Weise, T., Yang, Z., Tang, K.: Large-scale global optimization using cooperative coevolution with variable interaction learning. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6239, pp. 300–309. Springer, Heidelberg (2010)
Collet, P., Lutton, E., Raynal, F., Schoenauer, M.: Polar ifs + parisian genetic programming = efficient ifs inverse problem solving. Genetic Programming and Evolvable Machines Journal 1(4), 339–361 (2000)
De Jong, E.D., Stanley, K.O., Wiegand, R.P.: Introductory tutorial on coevolution. In: Proceedings of the 2007 GECCO Conference Companion on Genetic and Evolutionary Computation, London, United Kingdom (2007)
Dunn, E., Olague, G., Lutton, E.: Automated photogrammetric network design using the parisian approach. In: EvoIASP 2005, Lausanne, nominated for the best paper Award (2005)
Landrin-Schweitzer, Y., Collet, P., Lutton, E.: Introducing lateral thinking in search engines. GPEM, Genetic Programming an Evolvable Hardware Journal 1(7), 9–31 (2006); Banzhaf, W., et al (eds.)
Lutton, E., Olague, G.: Parisian camera placement for vision metrology. Pattern Recognition Letters 27(11), 1209–1219 (2006)
Ochoa, G., Lutton, E., Burke, E.K.: Cooperative royal road functions. In: Evolution Artificielle, Tours, France, October 29-31 (2007)
Panait, L., Luke, S., Harrison, J.F.: Archive-based cooperative coevolutionary algorithms. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, Seattle, Washington, USA (2006)
Popovici, E., De Jong, K.: The effects of interaction frequency on the optimization performance of cooperative coevolution. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, Seattle, Washington, USA (2006)
Potter, M.A., Couldrey, C.: A cooperative coevolutionary approach to partitional clustering. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6238, pp. 374–383. Springer, Heidelberg (2010)
Sanchez, E., Schillaci, M., Squillero, G.: Evolutionary Optimization: the μGP toolkit, 1st edn. Springer, Heidelberg (2011)
Sanchez, E., Squillero, G., Tonda, A.: Group evolution: Emerging synergy through a coordinated effort. In: Proceedings of the 2011 IEEE Congress of Evolutionary Computation, CEC (2011)
Vidal, F.P., Louchet, J., Rocchisani, J.-M., Lutton, É.: New genetic operators in the fly algorithm: Application to medical PET image reconstruction. In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcazar, A.I., Goh, C.-K., Merelo, J.J., Neri, F., Preuß, M., Togelius, J., Yannakakis, G.N. (eds.) EvoApplicatons 2010. LNCS, vol. 6024, pp. 292–301. Springer, Heidelberg (2010)
Wiegand, R.P., Potter, M.A.: Robustness in cooperative coevolution. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, Seattle, Washington, USA (2006)
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Tonda, A., Lutton, E., Squillero, G. (2011). Lamps: A Test Problem for Cooperative Coevolution. In: Pelta, D.A., Krasnogor, N., Dumitrescu, D., Chira, C., Lung, R. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2011). Studies in Computational Intelligence, vol 387. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24094-2_7
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DOI: https://doi.org/10.1007/978-3-642-24094-2_7
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