Abstract
This paper describes a new approach for evolving recurrent neural networks using Genetic Programming. A system has been developed to train weightless neural networks using construction rules. The network construction rules are evolved by the Genetic Programming system which build the solution neural networks. The use of rules allows networks to be constructed modularly. Experimentation with decomposable Boolean functions has revealed that the performance of the system is superior to a non-modular version of the system.
Bret Talko is now with the Defence Science and Technology Organisation in Australia. The authors would like to gratefully acknowledge the support of the Agent Laboratory in the department.
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Bret Talko. A rule-based approach for constructing neural networks using genetic programming. Master’s thesis, Department of Computer Science and Software Engineering, The University of Melbourne, Australia, March 1999.
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© 1999 Springer-Verlag Berlin Heidelberg
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Talko, B., Stern, L., Kitchen, L. (1999). Evolving Modular Neural Networks Using Rule-Based Genetic Programming. In: Foo, N. (eds) Advanced Topics in Artificial Intelligence. AI 1999. Lecture Notes in Computer Science(), vol 1747. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46695-9_47
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DOI: https://doi.org/10.1007/3-540-46695-9_47
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