Titlebook: Genetic Programming Theory and Practice X; Rick Riolo,Ekaterina Vladislavleva,Jason H. Moore Book 2013 Springer Science+Business Media New

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A New Mutation Paradigm for Genetic Programming,we compare different common values of the power-law exponent to the another self-adaptive mutation mechanism directly inspired by Simulated Annealing. We conclude that our novel method is a viable alternative to constant and self-adaptive mutation rates, especially because it tends to reduce the number of parameters of genetic programming.

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Evolving SQL Queries from Examples with Developmental Genetic Programming,hat a developmental genetic programming system can produce queries that are reasonably accurate while excelling in human comprehensibility relative to the well-known C5.0 decision tree generation system.

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https://doi.org/10.1007/978-3-531-91034-5f a number of different build options. As a proof-of-concept we compare three variations of evolutionary learning models for a color-following problem on a robot based on one of the designs: a simple neural network learning framework of the type typically seen in current research, a more extensive l

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https://doi.org/10.1007/978-3-8349-9654-1 or biological expert knowledge. The goal of the present study was to apply CES to the genetic analysis of prostate cancer aggressiveness in a large sample of European Americans. We introduce here the use of Pareto-optimization to help address overfitting in the learning system. We further introduce

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Heinz-Adalbert Krebs,Patricia Hagenweilermplex traits. In this chapter, we discuss the challenges in handling meta-dimensional data usinggrammatical evolution neural networks (GENN) which are one modeling component ofATHENA, and a characterization of the models identified in simulation studies to explore the ability of GENN to build comple

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