Use of genetic algorithms for encoding efficient neural network architectures: neurocomputer implementation

Jason James; Cihan H. Dagli

doi:10.1117/12.205139

6 April 1995 Use of genetic algorithms for encoding efficient neural network architectures: neurocomputer implementation

Jason James, Cihan H. Dagli

Author Affiliations +

Proceedings Volume 2492, Applications and Science of Artificial Neural Networks; (1995) https://doi.org/10.1117/12.205139
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

In this study an attempt is being made to encode the architecture of a neural network in a chromosome string for evolving robust, fast-learning, minimal neural network architectures through genetic algorithms. Various attributes affecting the learning of the network are represented as genes. The performance of the networks is used as the fitness value. Neural network architecture design concepts are initially demonstrated using a backpropagation architecture with the standard data set of Rosenberg and Sejnowski for text to speech conversion on Adaptive Solutions Inc.'s CNAPS Neuro-Computer. The architectures obtained are compared with the one reported in the literature for the standard data set used. The study concludes by providing some insights regarding the architecture encoding for other artificial neural network paradigms.

Citation Download Citation

Jason James and Cihan H. Dagli "Use of genetic algorithms for encoding efficient neural network architectures: neurocomputer implementation", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205139

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