Titlebook: Genetic Programming; 5th European Confere James A. Foster,Evelyne Lutton,Andrea Tettamanzi Conference proceedings 2002 Springer-Verlag Berl

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A New View on Symbolic Regressionduals reconstructed in this process. This new three dimensional representation allows the user to recognize certain possibilities to improve his setup of the process parameters. Furthermore this new representation allows a wider usage of the generated three dimensional objects with nearly every CAD

GENUS

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No Coercion and No Prohibition, a Position Independent Encoding Scheme for Evolutionary Algorithms –produce proteins that regulate the metabolic pathways of the cell. The phenotype is the behaviour of the cells metabolism, which corresponds to the development of the computer program in our case. In this procedure, the actual protein encoded by a gene is the same regardless of the position of the g

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Maintaining the Diversity of Genetic Programsty in genetic algorithms, but few investigations have been done for genetic programs. We define here a diversity measure for genetic programs based on our metric for genetic trees [.]. We use this distance measure for studying the effects of fitness sharing. We then propose a method for adaptively m

echnic

N-Version Genetic Programming via Fault Maskinged modules in such a way as to maximize their collective fault masking ability. The ensemble itself is an example of .-version modular redundancy for fault tolerance, where the output of the ensemble is the most frequent output of n independent modules. By maximizing collective fault masking, NVGP a

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An Analysis of Koza’s Computational Effort Statistic for Genetic Programmingf the effectiveness of sundry modifications to traditional GP. The statistic that is commonly used to report the amount of computational effort to solve a particular problem with 99% probability is Koza’s . statistic. This paper analyzes this measure from a statistical perspective. In particular, Ko

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