Abstract
A new graph-based evolutionary algorithm named “Genetic Network Programming, GNP” has been proposed. GNP represents its solutions as directed graph structures, which can improve the expression ability and performance. Since GA, GP and GNP already proposed are based on evolution and they cannot change their solutions until one generation ends, we propose GNP with Reinforcement Learning (GNP with RL) in this paper in order to search solutions quickly. Evolutionary algorithm of GNP makes very compact directed graph structure which contributes to reducing the size of the Q-table and saving memory. Reinforcement Learning of GNP improves search speed for solutions because it can use the information obtained during tasks.
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Katagiri, H., Hirasawa, K., Hu, J., Murata, J.: Network structure Oriented Evolutionary Model - Genetic Network Programming - and Its comparison with Genetic Programming. In: 2001 Genetic and Evolutionary Computation Conference, Late Breaking Papers, pp. 219–226 (2001)
Mabu, S., Hirasawa, K., Hu, J.: Genetic Network Programming with Learning and Evolution for Adapting to Dynamical Environments. In: Proc. of 2003 Congress on Evolutionary Computation, pp. 69–76 (2003)
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Mabu, S., Hirasawa, K., Hu, J. (2004). Genetic Network Programming with Reinforcement Learning and Its Performance Evaluation. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24855-2_81
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DOI: https://doi.org/10.1007/978-3-540-24855-2_81
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