Title

Genetically Engineered Art Architectures

Abstract

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. © 2007 Springer-Verlag Berlin Heidelberg.

Publication Date

7-9-2007

Publication Title

Studies in Computational Intelligence

Volume

67

Number of Pages

233-262

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-540-72687-6_12

Socpus ID

34347377134 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/34347377134

This document is currently not available here.

Share

COinS