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Genetic Programming Theory and Practice XVII

Wolfgang Banzhaf (Hrsg.), Leonardo Trujillo (Hrsg.), Erik Goodman (Hrsg.), Leigh Sheneman (Hrsg.), Bill Worzel (Hrsg.)

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Springer International Publishing img Link Publisher

Naturwissenschaften, Medizin, Informatik, Technik / Informatik

Beschreibung

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.  In this year’s edition, the topics covered include many of the most important issues and research questions in the field, such as: opportune application domains for GP-based methods, game playing and co-evolutionary search, symbolic regression and efficient learning strategies, encodings and representations for GP, schema theorems, and 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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Schlagwörter

Evolution of Models, Machine Learning, Data Analysis, algorithm analysis and problem complexity, Genetic Programming, Artificial Evolution, Genetic Programming Theory, symbolic classification, deep learning, Genetic Programming Applications, Program Induction, Symbolic Regression