Genetic Programming Theory and Practice XI

  • Rick Riolo
  • Jason H. Moore
  • Mark Kotanchek

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

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Michael F. Korns
    Pages 1-30
  3. Sean Stijven, Ruben Van den Bossche, Ekaterina Vladislavleva, Kurt Vanmechelen, Jan Broeckhove, Mark Kotanchek
    Pages 47-63
  4. Babak Hodjat, Erik Hemberg, Hormoz Shahrzad, Una-May O’Reilly
    Pages 65-83
  5. Conor Ryan, Joe Sullivan, Barry Fitzgerald
    Pages 85-100
  6. Philip Truscott, Michael F. Korns
    Pages 119-135
  7. Ilknur Icke, Nicholas A. Allgaier, Christopher M. Danforth, Robert A. Whelan, Hugh P. Garavan, Joshua C. Bongard
    Pages 155-173
  8. Michael Affenzeller, Stephan M. Winkler, Gabriel Kronberger, Michael Kommenda, Bogdan Burlacu, Stefan Wagner
    Pages 175-190
  9. Leonardo Vanneschi, Sara Silva, Mauro Castelli, Luca Manzoni
    Pages 191-209
  10. Ruowang Li, Emily R. Holzinger, Scott M. Dudek, Marylyn D. Ritchie
    Pages 211-224
  11. Back Matter
    Pages 225-227

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: 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.


Artificial evolution Evolution of models Feature selection Genetic programming Genetic programming applications Genetic programming theory Program induction Symbolic regression

Editors and affiliations

  • Rick Riolo
    • 1
  • Jason H. Moore
    • 2
  • Mark Kotanchek
    • 3
  1. 1.University of MichiganAnn ArborUSA
  2. 2.Inst for Quantitative Biomedical ScienceDartmouth Medical SchoolLebanonUSA
  3. 3.Evolved AnalyticsMidlandUSA

Bibliographic information