Titlebook: Genetic Programming; 4th European Confere Julian Miller,Marco Tomassini,William B. Langdon Conference proceedings 2001 Springer-Verlag Berl

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CAGE: A Tool for Parallel Genetic Programming Applicationse widespread belief that cellular model is not suitable for parallel genetic programming implementations, experimental results show a better convergence with respect to the island approach, a good scale-up behaviour and a nearly linear speed-up.

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Evolving Receiver Operating Characteristics for Data Fusiondividual ROCs [.]. As expected in at least some cases better ROCs can be produced. We show genetic programming (GP) can automatically produce a combination of classifiers whose ROC is better than the convex hull of the supplied classifier’s ROCs.

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Erratum to: Formeln und Hilfstabellen,stancy and plays an important role in computer vision research. We have used genetic programming to automatically search the space of programs to solve the problem of color constancy for an artificial retina. This retina consists of a two dimensional array of elements each capable of exchanging info

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Hans-Jürgen Wolter,Karin Wolff,Werner Freundgramming algorithm on simple symbolic regression problems. We measure the performance of a standard gp and two variants of saw extensions on two different symbolic regression problems from literature. Also, we propose a model for randomly generating polynomials which we then use to further test all

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Das v?lkerrechtliche Protektoratines genetic optimization and simulation methodology, to evaluate a multi-objective fitness function which includes both the suitability of the filter transfer function and the transition activity of digital blocks. The proper choice of fitness function and selection criteria allows the genetic algo

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