© 2019

Genetic Programming Theory and Practice XVI

  • Wolfgang Banzhaf
  • Lee Spector
  • Leigh Sheneman


  • 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 Genetic and Evolutionary Computation book series (GEVO)

Table of contents

  1. Front Matter
    Pages i-xxi
  2. Emily Dolson, Alexander Lalejini, Charles Ofria
    Pages 1-16
  3. Arend Hintze, Jory Schossau, Clifford Bohm
    Pages 17-36
  4. Gabriel Kronberger, Lukas Kammerer, Bogdan Burlacu, Stephan M. Winkler, Michael Kommenda, Michael Affenzeller
    Pages 85-102
  5. Blossom Metevier, Anil Kumar Saini, Lee Spector
    Pages 123-136
  6. Julian F. Miller, Dennis G. Wilson, Sylvain Cussat-Blanc
    Pages 137-178
  7. Leonardo Trujillo, Luis Muñoz, Uriel López, Daniel E. Hernández
    Pages 193-207
  8. 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.


Genetic Programming Genetic Programming Theory Genetic Programming Applications Symbolic Regression Evolution of Models Program Induction Artificial Evolution Machine Learning Data Analysis symbolic classification deep learning

Editors and affiliations

  • Wolfgang Banzhaf
    • 1
  • Lee Spector
    • 2
  • Leigh Sheneman
    • 3
  1. 1.Computer Science and EngineeringJohn R. Koza Chair, Michigan State UniversityEast LansingUSA
  2. 2.Cognitive ScienceHampshire CollegeAmherstUSA
  3. 3.Department of Computer Science and EngineeringMichigan State UniversityEast LansingUSA

Bibliographic information

Industry Sectors
Chemical Manufacturing
IT & Software
Consumer Packaged Goods
Materials & Steel
Finance, Business & Banking
Energy, Utilities & Environment
Oil, Gas & Geosciences