Abstract
Dynamic environments are becoming more and more popular in many applicative domains. A large amount of literature has appeared to date dealing with the problem of tracking the extrema of dynamically changing target functions, but relatively few material has been produced on the problem of reconstructing the shape, or more generally finding the equation, of dynamically changing target functions. Nevertheless, in many applicative domains, reaching this goal would have an extremely important impact. It is the case, for instance, of complex systems modelling, like for instance biological systems or systems of biochemical reactions, where one is generally interested in understanding what’s going on in the system over time, rather than following the extrema of some target functions. Last but not least, we also believe that being able to reach this goal would help researchers to have a useful insight on the reasons that cause the change in the system over time, or at least the pattern of this modification. This paper is intended as a first preliminary step in the attempt to fill this gap. We show that genetic programming with variable population size is able to adapt to the environment modifications much faster (i.e. using a noteworthy smaller amount of computational effort) than standard genetic programming using fixed population size. The suitability of this model is tested on a set of benchmarks based on some well known symbolic regression problems.
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References
Banzhaf, W., Langdon, W.B.: Some considerations on the reason of bloat. Genetic Programming and Evolvable Machines 3, 81–91 (2002)
Branke, J.: Memory enhanced evolutionary algorithms for changing optimization problems. In: Congress on Evolutionary Computation CEC 1999, vol. 3, pp. 1875–1882. IEEE, Los Alamitos (1999)
Branke, J.: Evolutionary approaches to dynamic environments - updated survey. In: GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, pp. 27–30 (2001)
Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer, Dordrecht (2001)
Branke, J.: Evolutionary approaches to dynamic optimization problems – introduction and recent trends. In: Branke, J. (ed.) GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, pp. 2–4 (2003)
Branke, J., Kauler, T., Schmidt, C., Schmeck, H.: A multi-population approach to dynamic optimization problems. In: Adaptive Computing in Design and Manufacturing, pp. 299–308. Springer, Heidelberg (2000)
Burke, E., Gustafson, S., Kendall, G., Krasnogor, N.: Advanced population diversity measures in genetic programming. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 341–350. Springer, Heidelberg (2002)
Clerc, M.: Particle Swarm Optimization. ISTE (2006)
Cobb, H.G.: An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments. Technical Report ADA229159, Naval Research Lab, Washington DC (1990)
Dasgupta, D., Mcgregor, D.R.: Nonstationary function optimization using the structured genetic algorithm. In: Parallel Problem Solving From Nature, pp. 145–154. Elsevier, Amsterdam (1992)
de França, F.O., Von Zuben, F.J., de Castro, L.N.: An artificial immune network for multimodal function optimization on dynamic environments. In: GECCO 2005: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, pp. 289–296. ACM, New York (2005)
Dempsey, I.: Grammatical Evolution in Dynamic Environments. PhD thesis, University College Dublin, Ireland (2007)
Fernandes, C., Ramos, V., Rosa, A.C.: Varying the population size of artificial foraging swarms on time varying landscapes. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS, vol. 3696, pp. 311–316. Springer, Heidelberg (2005)
Fernández, F., Tomassini, M., Vanneschi, L.: An empirical study of multipopulation genetic programming. Genetic Programming and Evolvable Machines 4(1), 21–52 (2003)
Fernández, F., Tomassini, M., Vanneschi, L.: Saving computational effort in genetic programming by means of plagues. In: Congress on Evolutionary Computation (CEC 2003), Canberra, Australia, pp. 2042–2049. IEEE Press, Piscataway (2003)
Fernández, F., Vanneschi, L., Tomassini, M.: The effect of plagues in genetic programming: A study of variable size populations. In: Ryan, C., et al. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 317–326. Springer, Heidelberg (2003)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
Goldberg, D.E., Smith, R.E.: Nonstationary function optimization using genetic algorithms with dominance and diploidy. In: ICGA, pp. 59–68 (1987)
Grefenstette, J.J.: Genetic algorithms for changing environments. In: Parallel Problem Solving from Nature, vol. 2, pp. 137–144 (1992)
Huang, C.-F., Rocha, L.M.: Tracking extrema in dynamic environments using a coevolutionary agent-based model of genotype edition. In: GECCO 2005: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, pp. 545–552. ACM, New York (2005)
Keijzer, M.: Improving symbolic regression with interval arithmetic and linear scaling. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 71–83. Springer, Heidelberg (2003)
Koza, J.R.: Genetic Programming. The MIT Press, Cambridge (1992)
Mori, N., Kita, H., Nishikawa, Y.: Adaptation to a changing environment by means of the feedback thermodynamical genetic algorithm. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 513–522. Springer, Heidelberg (1998)
Ng, K.P., Wong, K.C.: A new diploid scheme and dominance change mechanism for non-stationary function optimization. In: Proceedings of the 6th International Conference on Genetic Algorithms, pp. 159–166. Morgan Kaufmann Publishers Inc., San Francisco (1995)
Poli, R., Langdon, W.B., McPhee, N.F.: A field guide to genetic programming (2008), Published via, http://lulu.com , http://www.gp-field-guide.org.uk (With contributions by J. R. Koza)
Rand, W., Riolo, R.: The problem with a self-adaptative mutation rate in some environments: a case study using the shaky ladder hyperplane-defined functions. In: GECCO 2005: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, pp. 1493–1500. ACM, New York (2005)
Tanev, I.: Genetic programming incorporating biased mutation for evolution and adaptation of snakebot. Genetic Programming and Evolvable Machines 8(1), 39–59 (2007)
Tomassini, M., Vanneschi, L., Cuendet, J., Fernández, F.: A new technique for dynamic size populations in genetic programming. In: Proceedings of the 2004 IEEE Congress on Evolutionary Computation (CEC 2004), Portland, Oregon, USA, pp. 486–493. IEEE Press, Piscataway (2004)
Tsutsui, S., Fujimoto, Y., Ghosh, A.: Forking genetic algorithms: Gas with search space division schemes. Evol. Comput. 5(1), 61–80 (1997)
Vavak, F., Jukes, K., Fogarty, T.C.: Learning the local search range for genetic optimisation in nonstationary environments. In: IEEE Intl. Conf. on Evolutionary Computation ICEC 1997, pp. 355–360. IEEE Publishing, Los Alamitos (1997)
Yang, S.: Constructing dynamic test environments for genetic algorithms based on problem difficulty. In: Congress on Evolutionary Computation, CEC 2004, vol. 2, pp. 1262–1269. IEEE, Piscataway (2004)
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Vanneschi, L., Cuccu, G. (2011). Reconstructing Dynamic Target Functions by Means of Genetic Programming Using Variable Population Size. In: Madani, K., Correia, A.D., Rosa, A., Filipe, J. (eds) Computational Intelligence. IJCCI 2009. Studies in Computational Intelligence, vol 343. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20206-3_8
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DOI: https://doi.org/10.1007/978-3-642-20206-3_8
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