Summary. This chapter focuses on the evolution of ARTMAP architectures, using genetic algorithms, with the objective of improving generalization performance and alleviating the ART category proliferation problem. We refer to the resulting architectures as GFAM, GEAM, and GGAM. We demonstrate through extensive experimentation that evolved ARTMAP architectures exhibit good generalization and are of small size, while consuming reasonable computational effort to produce an optimal or a sub-optimal network. Furthermore, we compare the performance of GFAM, GEAM and GGAM with other competitive ARTMAP structures that have appeared in the literature and addressed the category proliferation problem in ART.
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Keywords
- Input Pattern
- Adaptive Resonance Theory
- Fuzzy ARTMAP
- Interconnection Weight
- Fuzzy Adaptive Resonance Theory
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© 2007 Springer-Verlag Berlin Heidelberg
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Al-Daraiseh, A., Kaylani, A., Georgiopoulos, M., Mollaghasemi, M., Wu, A.S., Anagnostopoulos, G. (2007). Genetically Engineered ART Architectures. In: Kaburlasos, V.G., Ritter, G.X. (eds) Computational Intelligence Based on Lattice Theory. Studies in Computational Intelligence, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72687-6_12
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DOI: https://doi.org/10.1007/978-3-540-72687-6_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72686-9
Online ISBN: 978-3-540-72687-6
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