Abstract
We discuss how to use a Genetic Regulatory Network as an evolutionary representation to solve a typical GP reinforcement problem, the pole balancing. The network is a modified version of an Artificial Regulatory Network proposed a few years ago, and the task could be solved only by finding a proper way of connecting inputs and outputs to the network. We show that the representation is able to generalize well over the problem domain, and discuss the performance of different models of this kind.
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Banzhaf, W., Beslon, G., Christensen, S., Foster, J.A., Képès, F., Lefort, V., Miller, J.F., Radman, M., Ramsden, J.J.: From artificial evolution to computational evolution: a research agenda. Nature Reviews Genetics 7, 729–735 (2006)
Kirschner, M., Gerhart, J.: The plausibility of life: Resolving Darwin’s dilemma. Yale University Press, New Haven (2005)
Hasty, J., McMillan, D., Isaacs, F., Collins, J.J.: Computational studies of gene regulatory networks: In numero molecular biology. Nature Reviews Genetics 2, 268–279 (2001)
Lones, M.A., Tyrrell, A.M.: Modelling biological evolvability: Implicit context and variation filtering in enzyme genetic programming. BioSystems 76(1-3), 229–238 (2004)
Zhan, S., Miller, J.F., Tyrrell, A.M.: An evolutionary system using development and artificial genetic regulatory networks. In: Proc. IEEE CEC 2008. IEEE Press, Los Alamitos (2008)
Banzhaf, W.: Artificial regulatory networks and genetic programming. In: Riolo, R., Worzel, B. (eds.) Genetic Programming Theory and Practice, ch. 4, pp. 43–62. Kluwer Publishers, Dordrecht (2003)
Wolfe, K., Shields, D.: Molecular evidence for an ancient duplication of the entire yeast genome. Nature 387, 708–713 (1997)
Kellis, M., Birren, B.W., Lander, E.S.: Proof and evolutionary analysis of ancient genome duplication in the yeast saccharomyces cerevisiae. Nature 428, 617–624 (2004)
Kuo, P.D., Banzhaf, W.: Small world and scale-free network topologies in an artificial regulatory network model. In: Pollack, J., et al. (eds.) Artificial Life IX, pp. 404–409. Bradford Books (2004)
Barto, A.G., Sutton, R.S., Anderson, C.W.: Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Transactions on Systems, Man and Cybernetics 13, 834–846 (1983)
Whitley, D., Dominic, S., Das, R., Anderson, C.W.: Genetic reinforcement learning for neurocontrol problems. Machine Learning 13(2-3), 259–284 (1993)
Rechenberg, I.: Evolutionsstrategie 1994. Frommann-Holzboog, Stuttgart (1994)
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Nicolau, M., Schoenauer, M., Banzhaf, W. (2010). Evolving Genes to Balance a Pole. In: Esparcia-Alcázar, A.I., Ekárt, A., Silva, S., Dignum, S., Uyar, A.Ş. (eds) Genetic Programming. EuroGP 2010. Lecture Notes in Computer Science, vol 6021. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12148-7_17
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DOI: https://doi.org/10.1007/978-3-642-12148-7_17
Publisher Name: Springer, Berlin, Heidelberg
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