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These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on realworld problems, producing a comprehensive view of the state of the art in GP. Topics in this volume include: multiobjective genetic programming, learning heuristics, Kaizen programming, Evolution of Everything (EvE), lexicase selection, behavioral program synthesis, symbolic regression with noisy training data, graph databases, and multidimensional clustering. It also covers several chapters on best practices and lesson learned from handson experience. Additional application areas include financial operations, genetic analysis, and predicting product choice. Readers will discover largescale, realworld applications of GP to a variety of problem domains via indepth presentations of the latest and most significant results.
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