Genetic Programming Theory and Practice XVI

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, General Computing, Programming
Cover of the book Genetic Programming Theory and Practice XVI by , Springer International Publishing
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Author: ISBN: 9783030047351
Publisher: Springer International Publishing Publication: January 23, 2019
Imprint: Springer Language: English
Author:
ISBN: 9783030047351
Publisher: Springer International Publishing
Publication: January 23, 2019
Imprint: Springer
Language: English

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: evolving developmental programs for neural networks solving multiple problems, tangled program, transfer learning and outlier detection using GP, program search for machine learning pipelines in reinforcement learning, automatic programming with GP, new variants of GP, like SignalGP, variants of lexicase selection, and symbolic regression and classification techniques. 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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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: evolving developmental programs for neural networks solving multiple problems, tangled program, transfer learning and outlier detection using GP, program search for machine learning pipelines in reinforcement learning, automatic programming with GP, new variants of GP, like SignalGP, variants of lexicase selection, and symbolic regression and classification techniques. 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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