Genetic Programming Theory and Practice X

  • Rick Riolo
  • Ekaterina Vladislavleva
  • Marylyn D Ritchie
  • Jason H. Moore

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

Table of contents

  1. Front Matter
    Pages i-xxvi
  2. Terence Soule, Robert B. Heckendorn
    Pages 15-29
  3. Simon Harding, Jürgen Leitner, Jürgen Schmidhuber
    Pages 31-44
  4. Christian Darabos, Mario Giacobini, Ting Hu, Jason H. Moore
    Pages 45-58
  5. Babak Hodjat, Hormoz Shahrzad
    Pages 59-71
  6. Una-May O’Reilly, Mark Wagy, Babak Hodjat
    Pages 73-85
  7. Jason H. Moore, Douglas P. Hill, Arvis Sulovari, La Creis Kidd
    Pages 87-101
  8. Marylyn D. Ritchie, Emily R. Holzinger, Scott M. Dudek, Alex T. Frase, Prabhakar Chalise, Brooke Fridley
    Pages 103-115
  9. Michael F. Korns
    Pages 117-137
  10. Flor A. Castillo, Carlos M. Villa, Arthur K. Kordon
    Pages 139-154
  11. Oliver Flasch, Thomas Bartz-Beielstein
    Pages 155-169
  12. Amit Benbassat, Achiya Elyasaf, Moshe Sipper
    Pages 171-185
  13. Mark E. Kotanchek, Ekaterina Vladislavleva, Guido Smits
    Pages 187-203
  14. James McDermott, Kalyan Veeramachaneni, Una-May O’Reilly
    Pages 205-221
  15. Erik Hemberg, Lester Ho, Michael O’Neill, Holger Claussen
    Pages 223-238
  16. Back Matter
    Pages 239-242

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.

Topics in this volume include: evolutionary constraints, relaxation of selection mechanisms, diversity preservation strategies, flexing fitness evaluation, evolution in dynamic environments, multi-objective and multi-modal selection, foundations of evolvability, evolvable and adaptive evolutionary operators, foundation of  injecting expert knowledge in evolutionary search, analysis of problem difficulty and required GP algorithm complexity, foundations in running GP on the cloud – communication, cooperation, flexible implementation, and ensemble methods. Additional focal points for GP symbolic regression are: (1) The need to guarantee convergence to solutions in the function discovery mode; (2) Issues on model validation; (3) The need for model analysis workflows for insight generation based on generated GP solutions – model exploration, visualization, variable selection, dimensionality analysis; (4) Issues in combining different types of data.

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

Artificial Evolution Evolution of Models Feature Selection Genetic Programing Applications Genetic Programming Genetic Programming Theory Program Induction Symbolic Regression

Editors and affiliations

  • Rick Riolo
    • 1
  • Ekaterina Vladislavleva
    • 2
  • Marylyn D Ritchie
    • 3
  • Jason H. Moore
    • 4
  1. 1.Center for the Study of Complex SystemsUniversity of MichiganAnn ArborUSA
  2. 2.Evolved Analytics Europe BVBABeerseBelgium
  3. 3., Department of Biochemistry and MolecularThe Pennsylvania State UniversityUniversity ParkUSA
  4. 4., Institute for QuantitativeDartmouth Medical SchoolLebanonUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4614-6846-2
  • Copyright Information Springer Science+Business Media New York 2013
  • Publisher Name Springer, New York, NY
  • eBook Packages Computer Science
  • Print ISBN 978-1-4614-6845-5
  • Online ISBN 978-1-4614-6846-2
  • Series Print ISSN 1932-0167
  • About this book
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