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Title: A lateral symmetry approach to percentage based hybrid pattern (PHP) training
Authors: Guan, SU
Ramanathan, K
Keywords: Genetic algorithm;Training pattern;Hybrid training;Pattern learning;Training parameters;Supervised learning
Issue Date: 2007
Publisher: Freund & Pettman
Citation: Journal of Intelligent Systems. 16 (3) 241-273
Abstract: In this paper, we investigate the application of lateral symmetry to supervised learning using genetic algorithms. The hypothesis is motivated by the presence of symmetry in the animal brain and by research results which show approximately equal task division between the two hemispheres of the brain. In this paper, each training pattern is considered to be a task. By applying the concept of lateral symmetry, we use global training (a typically right brained activity) to learn half the tasks and local training (a left brained activity) to learn the rest of the tasks. We verified the use of this Percentage-based Pattern (PHP) training approach using various comprehensive programs and also applied this approach to genetic algorithm based curve fitting problems. The results in both cases were encouraging. PHP based hybrid algorithms resulted in significant reduction in the testing error as well as in the training time. The PHP algorithm is therefore concluded to be an approach towards more...
ISSN: 0334-1860
Appears in Collections:Electronic and Computer Engineering
Dept of Electronic and Computer Engineering Research Papers

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