Titlebook: Genetic Programming Theory and Practice XII; Rick Riolo,William P. Worzel,Mark Kotanchek Book 2015 Springer International Publishing Switz

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Book 2015ion, and highly distributed genetic programming systems. Application areas include chemical process control, circuit design, financial data mining and bioinformatics. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.

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Rick Riolo,William P. Worzel,Mark KotanchekProvides papers describing cutting-edge work on genetic programming theory, applications of GP and theory.Offers large-scale, real-world applications of GP to a variety of problem domains, from financ

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Gunter Schlageter,Wolffried Stuckyhese large-scale datasets make it possible and necessary to implement machine learning techniques for mining biological insights. In this chapter, we describe several examples to show how machine learning approaches are used to elucidate the mechanism of transcriptional regulation mediated by transc

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Programmentwicklung im dBASE-Formatuence risk in the context of our local ecology. The complexity of the genotype to phenotype mapping relationship for common diseases like POAG necessitates analytical approaches that move beyond parametric statistical methods such as logistic regression that assume a particular mathematical model. T

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Patricia Deflorin,Maike Scherrer,Toni W?flerrograms. Here we extend the biological analogy to incorporate epigenetic regulation through both learning and evolution. We begin the chapter with a discussion of Darwinian, Lamarckian, and Baldwinian approaches to evolutionary computation and describe how recent findings in biology differ conceptua

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https://doi.org/10.1007/978-3-658-38585-9lity of GP depends on the representation of programs in the population and how to handle illegal or type incoherent expressions that arise from crossover and mutation within a population of programs. The SKGP is a GP system that uses graphs of . to represent functions and a strong type system to inf

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https://doi.org/10.1007/978-3-658-23240-5od is inspired by the sequential covering strategy from machine learning, but instead of sequentially reducing the size of the problem being solved, it sequentially transforms the original problem into potentially simpler problems. This transformation is performed according to the semantic distances

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https://doi.org/10.1007/978-3-658-14445-6window defines the portion of the data visible to the algorithm during training and is moved over the data. The window is moved regularly based on the generations or on the current selection pressure when using offspring selection. The sliding window technique has the effect that population has to a