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Constraints on task and search complexity in GA+NN models of learning and adaptive behaviour

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Book cover Evolutionary Computing (AISB EC 1995)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 993))

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Abstract

Models of adaptive behaviour are normally highly complex due to a greater emphasis on, learning by interaction with the environment, flexible behaviour, and, an evolutionary learning methodology. This research investigates how a combination of Genetic Algorithms and Neural Networks (GA+NN) can be used to model behaviour to solve a task requiring co-operation between two artificial autonomous agents. In particular, the results illustrate how a complex learning task (that is, a simple game of football which requires a highly dynamic interaction between agents and the environment) is learned more efficiently as a result of GA operator modifications and modularisation of the learning task. The overall effect of these changes is to constrain the search space that the hybrid GA+NN system can potentially explore. A distinction between task and search complexity provides a useful framework for a clearer comprehension of the nature of constraints vis-a-vis the modelling process (generally characterised as a complex adaptive system). Broad implications of the findings on modular models of adaptive behaviour and the role of constraints on complexity are briefly discussed.

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Authors and Affiliations

  1. Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), P.O.Box 1385, GR 711 10, Heraklion, Crete

    Mukesh J. Patel

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  1. Mukesh J. Patel
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Terence C. Fogarty

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© 1995 Springer-Verlag Berlin Heidelberg

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Patel, M.J. (1995). Constraints on task and search complexity in GA+NN models of learning and adaptive behaviour. In: Fogarty, T.C. (eds) Evolutionary Computing. AISB EC 1995. Lecture Notes in Computer Science, vol 993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60469-3_36

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  • DOI: https://doi.org/10.1007/3-540-60469-3_36

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60469-3

  • Online ISBN: 978-3-540-47515-6

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