Learning experiments with genetic optimization of a generalized regression neural network |
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Authors: | James V Hansen Rayman D Meservy |
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Abstract: | This paper reports a study unifying optimization by genetic algorithm with a generalized regression neural network. Experiments compare hill-climbing optimization with that of a genetic algorithm, both in conjunction with a generalized regression neural network. Controlled data with nine independent variables are used in combination with conjunctive and compensatory decision forms, having zero percent and 10 percent noise levels. Results consistently favor the GRNN unified with the genetic algorithm. |
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Keywords: | Genetic algorithm Generalized regression neural network Radial basis function |
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