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In the vast majority of genetic algorithm implementations, the operator probabilities are xed throughout a given run. However, it can be convincingly argued that these probabilities should vary over the course of a genetic algorithm run | so as to account for changes in the ability of the operators to produce children of increased tness. This dissertation(More)
John Holland has shown that when adaptive algorithms are used to search certain kinds of extremely large problem spaces, they will converge on a "good" solution fairly quickly. Such problem spaces are characterized by a low degree of epistasis. A host of classical search problems, however, are epistatic in nature. The present paper describes some new(More)
Multilayered feedforward neural networks possess a number of properties which make them particularly suited to complex pattern classification problems. However, their application to some real-world problems has been hampered by the lack of a training algonthm which reliably finds a nearly globally optimal set of weights in a relatively short time. Genetic(More)