## Artificial Neural Nets and Genetic AlgorithmsSpringer-Verlag, 1995 - Neural networks (Computer science) |

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### Contents

A promising classifier system | 14 |

Timetabling using genetic algorithms | 30 |

a dynamic incremental network that learns by discrimination | 45 |

Copyright | |

33 other sections not shown

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### Common terms and phrases

adaptive alleles analysis application approach architecture Artificial Neural Networks back-propagation basis function binary boolean boolean functions cells chromosome classification coefficients components Computer connections constraints convergence corresponding cost crossover crossover operator data set defined distribution dynamic encoding error estimate evaluation evolution evolutionary evolutionary algorithms example fault feedforward neural network Figure fitness function gene genetic algorithm genetic operators genotype given heuristic hidden layer IEEE implementation initial input instance iterations Kohonen learning algorithm linear machine matrix method module mutation neurons nodes nonlinear obtained optimal output paper parallel parameters patterns perceptron performance population presented problem Proc procedure proposed radial basis function random randomly RBF network recognition representation represented samples schemata scheme selected sequence signal simulated annealing solution string structure subset supervised learning task techniques Theorem tion topology training set transformed units variables wavelet weight vector