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- W92617545 abstract "This paper introduces a new algorithm which uses a genetic algorithm (GA) to determine the topology and link weights of a neural network. If the genetic algorithm fails to find a satisfactory solution network, the best network developed by the GA is used to try to find a solution via back-propagation. In this way, each algorithm is used to its greatest advantage: the GA (with its global search) determines a (sub-optimal) topology and weights to solve the problem, and back-propagation (with its local search) seeks the best solution in the area of the weight and topology spaces found by the GA. The intent is to develop an algorithm which can be used as a first attempt to solve unknown problems. If a solution is not found immediately by the algorithm, at least an appreciable amount of information about the solution can be gleaned from its results, and a lot of the initial guesswork that currently exists in finding a neural network solution to a problem can be eliminated. Some of the features of the GANNet algorithm are:" @default.
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- W92617545 date "1993-01-01" @default.
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- W92617545 title "GANNet: A genetic algorithm for optimizing topology and weights in neural network design" @default.
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- W92617545 doi "https://doi.org/10.1007/3-540-56798-4_167" @default.
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