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Titlebook: Evolutionary Computing; AISB Workshop, Brigh Terence C. Fogarty Conference proceedings 1996 Springer-Verlag Berlin Heidelberg 1996 artifici

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Fast evolutionary learning of minimal radial basis function neural networks using a genetic algoritilised are adapted from those used in recent work on evolution of fuzzy inference systems. A parsimonious allocation of training sets and training epochs to evaluation of candidate networks during evolution is proposed in order to accelerate the learning process.
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Genetic Programming for feature detection and image segmentation, used to highly enhance and detect features of interest or to build pixel-classification-based segmentation algorithms. Some experiments with medical images which show the efficacy of the approach are reported.
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,Investigating multiploidy’s niche, cases where a normal GA would be likely to irretrievably lose important genetic material. Here we continute this investigation in the context of more realistic problems: the multiple knapsack problem, and the set covering problem. There are many complex effects, but experiments tend overall to reinforce the above suggestion.
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Evaluating Family Mental Healthloit the problem structure in order to define appropriate heuristics in the proposed search framework..Another possible line for future research could be the utilisation of our search framework as a decision support tool that would interactively assist in the global optimization process.
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,Evolving software test data — GA’s learn self expression,cedures, and the relationship that this has to the quality and size of the test sets evolved, in order to assess the scalability of the evolutionary approach to “real-world” problems, and the factors that would need to be taken into consideration when designing systems for the automatic generation of test cases.
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