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Genetic Programming Theory and Practice XVII

  • Wolfgang Banzhaf
  • Erik Goodman
  • Leigh Sheneman
  • Leonardo Trujillo
  • Bill Worzel
Book

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

Table of contents

  1. Front Matter
    Pages i-xxvi
  2. Austin J. Ferguson, Jose Guadalupe Hernandez, Daniel Junghans, Alexander Lalejini, Emily Dolson, Charles Ofria
    Pages 1-23
  3. Francisco Fernández de Vega, Gustavo Olague, Francisco Chávez, Daniel Lanza, Wolfgang Banzhaf, Erik Goodman
    Pages 25-38
  4. Lukas Kammerer, Gabriel Kronberger, Bogdan Burlacu, Stephan M. Winkler, Michael Kommenda, Michael Affenzeller
    Pages 79-99
  5. Stephen Kelly, Wolfgang Banzhaf
    Pages 101-119
  6. Douglas Kirkpatrick, Arend Hintze
    Pages 121-143
  7. Arthur Kordon, Theresa Kotanchek, Mark Kotanchek
    Pages 145-163
  8. Joel Lehman
    Pages 181-200
  9. Gustavo Olague, Mariana Chan-Ley
    Pages 227-253
  10. Edward Pantridge, Thomas Helmuth, Lee Spector
    Pages 255-274
  11. Hormoz Shahrzad, Babak Hodjat, Camille Dollé, Andrei Denissov, Simon Lau, Donn Goodhew et al.
    Pages 275-293
  12. Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
    Pages 295-305
  13. Andrew N. Sloss, Steven Gustafson
    Pages 307-344
  14. Robert J. Smith, Malcolm I. Heywood
    Pages 345-366
  15. David R. White, Benjamin Fowler, Wolfgang Banzhaf, Earl T. Barr
    Pages 367-381
  16. Yuan Yuan, Wolfgang Banzhaf
    Pages 383-406
  17. Back Matter
    Pages 407-409

About this book

Introduction

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.



Keywords

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
  • Erik Goodman
    • 2
  • Leigh Sheneman
    • 3
  • Leonardo Trujillo
    • 4
  • Bill Worzel
    • 5
  1. 1.Computer Science and EngineeringJohn R. Koza Chair, Michigan State UniversityEast LansingUSA
  2. 2.BEACON CenterMichigan State UniversityEast LansingUSA
  3. 3.Department of Computer Science and EngineeringMichigan State UniversityOkemosUSA
  4. 4.Depto Ingenieria en Electronic Electrica Tecnológico Nacional de México/ ITTijuanaMexico
  5. 5.Evolution EnterprisesAnn ArborUSA

Bibliographic information