Titlebook: Genetic Programming Theory and Practice XVII; Wolfgang Banzhaf,Erik Goodman,Bill Worzel Book 2020 Springer Nature Switzerland AG 2020 Gene

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Book 2020and new selection mechanisms.The volume includes several chapters on best practices and lessons learned from hands-on experience. 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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,Durchführung der Felduntersuchungen,advantages of Plush while providing additional benefits. These results illustrate the virtues of unconstrained linear genome representations more generally, and may be transferable to genetic programming systems that target different languages for evolved programs.

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Explorations of the Semantic Learning Machine Neuroevolution Algorithm: Dynamic Training Data Use, nature of SLM already confers enough diversity to the ensembles such that Bagging and Boosting cannot improve upon a simple averaging ensemble construction method. Finally, some initial results with SLM and Convolutional NNs are presented and future Deep Learning perspectives are discussed.

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Can Genetic Programming Perform Explainable Machine Learning for Bioinformatics?, bioinformatics classification problem. We developed feature selection analysis methods and aimed at explaining which features are influential in the prediction, and whether such an influence is through individual effects or synergistic effects of combining with other features.

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