@inbook{8d867bd0b8d94aaf889f3c1f076590c5,
title = "Genetic network programming with reinforcement learning and its performance evaluation",
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.",
author = "Shingo Mabu and Kotaro Hirasawa and Jinglu Hu",
year = "2004",
doi = "10.1007/978-3-540-24855-2_81",
language = "English",
isbn = "3540223436",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "710--711",
editor = "Riccardo Poli and Owen Holland and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Lanzi, {Pier Luca} and Lee Spector and Tettamanzi, {Andrea G. B.} and Dirk Thierens and Tyrrell, {Andrew M.}",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
}