Title :
Genetic algorithms as a tool for the analysis of adaptive resonance theory network training sets
Author :
Caudell, Thomas P.
Author_Institution :
Boeing Comput. Services, Res. & Technol., Seattle, WA, USA
fDate :
6/6/1992 12:00:00 AM
Abstract :
Genetic algorithms (GAs) are used to study the basins of attraction for adaptive resonances theory (ART) neural networks. Two GAs are used to study attractors formed by the `bottom-up´ and the `top-down´ subsystems of a single binary input/binary output ART (ART1) neural network. The author presents results based on a simple training data set consisting of two distributions of centered rectangles. These results will ultimately aid in the refinement of training sets being used in neural information retrieval applications
Keywords :
genetic algorithms; learning (artificial intelligence); neural nets; ART1; adaptive resonance theory network training sets; attractors; basins of attraction; bottom-up; centered rectangles; genetic algorithms; neural information retrieval; single binary input/binary output ART neural net; top-down; Adaptive systems; Algorithm design and analysis; Costs; Design automation; Design engineering; Genetic algorithms; Group technology; Neural networks; Resonance; Subspace constraints;
Conference_Titel :
Combinations of Genetic Algorithms and Neural Networks, 1992., COGANN-92. International Workshop on
Conference_Location :
Baltimore, MD
Print_ISBN :
0-8186-2787-5
DOI :
10.1109/COGANN.1992.273939
