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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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書目名稱Genetic Programming Theory and Practice X
編輯Rick Riolo,Ekaterina Vladislavleva,Jason H. Moore
視頻videohttp://file.papertrans.cn/383/382605/382605.mp4
叢書名稱Genetic and Evolutionary Computation
圖書封面Titlebook: Genetic Programming Theory and Practice X;  Rick Riolo,Ekaterina Vladislavleva,Jason H. Moore Book 2013 Springer Science+Business Media New
描述.These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. .Topics in this volume include: evolutionary constraints, relaxation of selection mechanisms, diversity preservation strategies, flexing fitness evaluation, evolution in dynamic environments, multi-objective and multi-modal selection, foundations of evolvability, evolvable and adaptive evolutionary operators, foundation of ?injecting expert knowledge in evolutionary search, analysis of problem difficulty and required GP algorithm complexity, foundations in running GP on the cloud – communication, cooperation, flexible implementation, and ensemble methods. Additional focal points for GP symbolic regression are: (1) The need to guarantee convergence to solutions in the function discovery mode; (2) Issues on model validation; (3) The need for model analysis workflows for insight generation based on generated GP solutions – model exploration, visualization, variable selection, dimensionality analysis; (4) Issues in combinin
出版日期Book 2013
關(guān)鍵詞Artificial Evolution; Evolution of Models; Feature Selection; Genetic Programing Applications; Genetic P
版次1
doihttps://doi.org/10.1007/978-1-4614-6846-2
isbn_softcover978-1-4939-0068-8
isbn_ebook978-1-4614-6846-2Series ISSN 1932-0167 Series E-ISSN 1932-0175
issn_series 1932-0167
copyrightSpringer Science+Business Media New York 2013
The information of publication is updating

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Cartesian Genetic Programming for Image Processing, image processing. We successfully demonstrate our new approach on several different problem domains. We show that the approach is fast, scalable and robust. In addition, by virtue of using off-the-shelf image processing libraries we can generate human readable programs that incorporate sophisticate
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A New Mutation Paradigm for Genetic Programming, be often observed. This implies that, while the vast majority of the strides will be short, on rare occasions, the strides are gigantic. We propose a mutation mechanism in Linear Genetic Programming inspired by this ethological behavior, thus obtaining a self-adaptive mutation rate. We experimental
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Introducing an Age-Varying Fitness Estimation Function,s a number of partial evaluations based on incomplete information or uncertainties. We show how this method can yield results that are close to similar methods where fitness is measured over the entire dataset, but at a fraction of the speed or memory usage, and in a parallelizable manner. We descri
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Meta-Dimensional Analysis of Phenotypes Using the Analysis Tool for Heritable and Environmental Netth these technological advances, we have made some progress in the identification of genes and proteins associated with common, complex human diseases. Still, our understanding of the genetic architecture of complex traits remains limited and additional research is needed to illuminate the genetic a
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A Baseline Symbolic Regression Algorithm,history for an academic field which is progressing rapidly. The original published symbolic regression algorithms in (Koza 1994) have long since been replaced by techniques such as pareto front, age layered population structures, and even age pareto front optimization. The lack of specific technique
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