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  • © 2019

Genetic Programming Theory and Practice XVI

  • Provides papers describing cutting-edge work on the theory and applications of genetic programming (GP)
  • 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
  • Explores controlled semantics, lexicase and other selection methods, crossover techniques, diversity analysis and understanding of convergence tendencies

Part of the book series: Genetic and Evolutionary Computation (GEVO)

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Table of contents (11 chapters)

  1. Front Matter

    Pages i-xxi
  2. Exploring Genetic Programming Systems with MAP-Elites

    • Emily Dolson, Alexander Lalejini, Charles Ofria
    Pages 1-16
  3. The Evolutionary Buffet Method

    • Arend Hintze, Jory Schossau, Clifford Bohm
    Pages 17-36
  4. Cluster Analysis of a Symbolic Regression Search Space

    • Gabriel Kronberger, Lukas Kammerer, Bogdan Burlacu, Stephan M. Winkler, Michael Kommenda, Michael Affenzeller
    Pages 85-102
  5. Lexicase Selection Beyond Genetic Programming

    • Blossom Metevier, Anil Kumar Saini, Lee Spector
    Pages 123-136
  6. Evolving Developmental Programs That Build Neural Networks for Solving Multiple Problems

    • Julian F. Miller, Dennis G. Wilson, Sylvain Cussat-Blanc
    Pages 137-178
  7. Untapped Potential of Genetic Programming: Transfer Learning and Outlier Removal

    • Leonardo Trujillo, Luis Muñoz, Uriel López, Daniel E. Hernández
    Pages 193-207
  8. Program Search for Machine Learning Pipelines Leveraging Symbolic Planning and Reinforcement Learning

    • Fangkai Yang, Steven Gustafson, Alexander Elkholy, Daoming Lyu, Bo Liu
    Pages 209-231
  9. Back Matter

    Pages 233-234

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. 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.


Editors and Affiliations

  • Computer Science and Engineering, John R. Koza Chair, Michigan State University, East Lansing, USA

    Wolfgang Banzhaf

  • Cognitive Science, Hampshire College, Amherst, USA

    Lee Spector

  • Department of Computer Science and Engineering, Michigan State University, East Lansing, USA

    Leigh Sheneman

Bibliographic Information

Buy it now

Buying options

eBook USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access