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- W1552447101 abstract "An arbitrary neural network has a number of functionally equivalent other networks. This causes redundancy in genetic representation of neural networks, which considerably undermines the merit of crossover in GAs [1]. This problem has received considerable attention in the past and has also been called the “competing conventions” problem [2]. We transform each neural network to an isomorphic neural network to maximize the genotypic consistency of two parents. We aim to develop a better genetic algorithm for neural network optimization by helping crossover preserve common functional characteristics of the two parents. This is achieved by protecting “phenotypic” consistency and, consequently, preserving building blocks with promising schemata." @default.
- W1552447101 created "2016-06-24" @default.
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- W1552447101 date "2004-01-01" @default.
- W1552447101 modified "2023-09-25" @default.
- W1552447101 title "Neural Network Normalization for Genetic Search" @default.
- W1552447101 cites W1653273292 @default.
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- W1552447101 doi "https://doi.org/10.1007/978-3-540-24855-2_42" @default.
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