Titlebook: Genetic Programming Theory and Practice II; Una-May O’Reilly,Tina Yu,Bill Worzel Book 2005 Springer-Verlag US 2005 Algorithms.Automat.algo

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Data Science and Big Data Computingds, these technologies are less successful at ranking true hits correctly by binding free energy. This chapter presents the automated removal of false positives from virtual hit sets, by evolving a post docking filter using Cartesian Genetic Programming(CGP). We also investigate characteristics of C

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Data Science for Economics and Financeurrent methods of designing and optimizing antennas by hand are time and labor intensive, limit complexity, and require significant expertise and experience. Evolutionary design techniques can overcome these limitations by searching the design space and automatically finding effective solutions that

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Usman Qamar,Muhammad Summair Razablem before applying it to the more difficult one..The chapter examines the dynamics of the library internals, and how functions compete for dominance of the library. We demonstrate that the libraries tend to converge on a small number of functions, and identify methods to test how well a library is likely to be able to scale.

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Classification Using Decision Trees,ed sustainable genetic programming technique - quick hierarchical fair competition (QHFC)- is used to evolve robust high-pass analog filters. It is shown that topological innovation by genetic programming can be used to improve the robustness of evolved design solutions with respect to both parameter perturbations and topology faults.

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Using Genetic Programming in Industrial Statistical Model Building,antly reduced. In case of designed data Genetic Programming reduced costs by suggesting transformations as an alternative to doing additional experimentation. In case of undesigned data Genetic Programming was instrumental in reducing the model building costs by providing alternative models for consideration.