Titlebook: Genetic Algorithms for Machine Learning; John J. Grefenstette Book 1994 Springer Science+Business Media New York 1994 algorithms.control.d

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Introduction,It is my pleasure to introduce this third Special Issue on Genetic Algorithms (GAs). The articles presented here were selected from preliminary versions presented at the International Conference on Genetic Algorithms in June 1991, as well as at a special Workshop on Genetic Algorithms for Machine Learning at the same Conference.

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Dermatological Disorders and Artifacts,We describe and evaluate a GA-based system called GABIL that continually learns and refines concept classification rules from its interaction with the environment. The use of GAs is motivated by recent studies showing the effects of various forms of bias built into different concept learning systems

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Diabetes Mellitus and Glucagonoma,le attention of the genetic algorithm community. The full-memory approach developed here uses the same high-level descriptive language that is used in rule-based systems. This allows for an easy utilization of inference rules of the well-known inductive learning methodology, which replace the tradit

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Book 1994l as at a special Workshop on GeneticAlgorithms for Machine Learning at the same Conference. .Genetic algorithms are general-purpose search algorithms that useprinciples inspired by natural population genetics to evolve solutionsto problems. The basic idea is to maintain a population of knowledgestr

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Melanoma Prognosis and Staging,ive results with AHC, another well-known reinforcement learning paradigm for neural networks that employs the temporal difference method. These algorithms are compared in terms of learning rates, performance-based generalization, and control behavior over time.

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