Genetic network programming with reinforcement learning and its performance evaluation

Shingo Mabu, Kotaro Hirasawa, Jinglu Hu

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsRiccardo Poli, Owen Holland, Wolfgang Banzhaf, Hans-Georg Beyer, Edmund Burke, Paul Darwen, Dipankar Dasgupta, Dario Floreano, James Foster, Mark Harman, Pier Luca Lanzi, Lee Spector, Andrea G. B. Tettamanzi, Dirk Thierens, Andrew M. Tyrrell
PublisherSpringer Verlag
Pages710-711
Number of pages2
ISBN (Print)3540223436
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3103
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Genetic network programming with reinforcement learning and its performance evaluation'. Together they form a unique fingerprint.

  • Cite this

    Mabu, S., Hirasawa, K., & Hu, J. (2004). Genetic network programming with reinforcement learning and its performance evaluation. In R. Poli, O. Holland, W. Banzhaf, H-G. Beyer, E. Burke, P. Darwen, D. Dasgupta, D. Floreano, J. Foster, M. Harman, P. L. Lanzi, L. Spector, A. G. B. Tettamanzi, D. Thierens, & A. M. Tyrrell (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 710-711). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3103). Springer Verlag. https://doi.org/10.1007/978-3-540-24855-2_81