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Genetic and Evolutionary Computation

Genetic Programming Theory and Practice XVII

Editors: BANZHAF, W., Goodman, E., Sheneman, L., Trujillo, L., Worzel, B. (Eds.)

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  • Provides contributions describing cutting-edge work on the theory and applications of genetic programming (GP)
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  • Offers large-scale, real-world applications (big data) of GP to a variety of problem domains, including commercial and scientific applications as well as financial and insurance problems
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  • Explores controlled semantics, lexicase and other selection methods, crossover techniques, diversity analysis and understanding of convergence tendencies
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About this book

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.



Table of contents (19 chapters)

Table of contents (19 chapters)
  • Characterizing the Effects of Random Subsampling on Lexicase Selection

    Pages 1-23

    Ferguson, Austin J. (et al.)

  • It Is Time for New Perspectives on How to Fight Bloat in GP

    Pages 25-38

    Vega, Francisco Fernández (et al.)

  • Explorations of the Semantic Learning Machine Neuroevolution Algorithm: Dynamic Training Data Use, Ensemble Construction Methods, and Deep Learning Perspectives

    Pages 39-62

    Gonçalves, Ivo (et al.)

  • Can Genetic Programming Perform Explainable Machine Learning for Bioinformatics?

    Pages 63-77

    Hu, Ting

  • Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication

    Pages 79-99

    Kammerer, Lukas (et al.)

Buy this book

eBook $109.00
price for USA in USD
  • ISBN 978-3-030-39958-0
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $149.99
price for USA in USD
  • ISBN 978-3-030-39957-3
  • Free shipping for individuals worldwide
  • Immediate ebook access, if available*, with your print order
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Genetic Programming Theory and Practice XVII
Editors
  • WOLFGANG BANZHAF
  • Erik Goodman
  • Leigh Sheneman
  • Leonardo Trujillo
  • Bill Worzel
Series Title
Genetic and Evolutionary Computation
Copyright
2020
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-39958-0
DOI
10.1007/978-3-030-39958-0
Hardcover ISBN
978-3-030-39957-3
Series ISSN
1932-0167
Edition Number
1
Number of Pages
XXVI, 409
Number of Illustrations
30 b/w illustrations, 112 illustrations in colour
Topics

*immediately available upon purchase as print book shipments may be delayed due to the COVID-19 crisis. ebook access is temporary and does not include ownership of the ebook. Only valid for books with an ebook version. Springer Reference Works are not included.