Genetic Programming Theory and Practice XIV

  • Rick Riolo
  • Bill Worzel
  • Brian Goldman
  • Bill Tozier

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

Table of contents

  1. Front Matter
    Pages i-xv
  2. Stephan M. Winkler, Michael Affenzeller, Bogdan Burlacu, Gabriel Kronberger, Michael Kommenda, Philipp Fleck
    Pages 1-17
  3. Erik Hemberg, Jacob Rosen, Una-May O’Reilly
    Pages 35-51
  4. Itay Azaria, Achiya Elyasaf, Moshe Sipper
    Pages 53-63
  5. Nicholas Freitag McPhee, Mitchell D. Finzel, Maggie M. Casale, Thomas Helmuth, Lee Spector
    Pages 65-83
  6. Thomas Helmuth, Lee Spector, Nicholas Freitag McPhee, Saul Shanabrook
    Pages 85-100
  7. Ting Hu, Wolfgang Banzhaf
    Pages 101-117
  8. Leonardo Trujillo, Emigdio Z-Flores, Perla S. Juárez-Smith, Pierrick Legrand, Sara Silva, Mauro Castelli et al.
    Pages 119-137
  9. Babak Hodjat, Hormoz Shahrzad, Risto Miikkulainen, Lawrence Murray, Chris Holmes
    Pages 139-148
  10. Luiz Otavio V. B. Oliveira, Fernando E. B. Otero, Gisele L. Pappa
    Pages 179-195
  11. Steven Gustafson, Arun Subramaniyan, Aisha Yousuf
    Pages 197-210
  12. 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. Chapters in this volume include: 

  • Similarity-based Analysis of Population Dynamics in GP Performing Symbolic Regression
  • Hybrid Structural and Behavioral Diversity Methods in GP
  • Multi-Population Competitive Coevolution for Anticipation of Tax Evasion
  • Evolving Artificial General Intelligence for Video Game Controllers
  • A Detailed Analysis of a PushGP Run
  • Linear Genomes for Structured Programs
  • Neutrality, Robustness, and Evolvability in GP
  • Local Search in GP
  • PRETSL: Distributed Probabilistic Rule Evolution for Time-Series Classification
  • Relational Structure in Program Synthesis Problems with Analogical Reasoning
  • An Evolutionary Algorithm for Big Data Multi-Class Classification Problems
  • A Generic Framework for Building Dispersion Operators in the Semantic Space
  • Assisting Asset Model Development with Evolutionary Augmentation
  • Building Blocks of Machine Learning Pipelines for Initialization of a Data Science Automation Tool 

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 Feature selection Artificial General Intelligence Distributed Probabilistic Rule Dispersion Operators Evolutionary Augmentation Analogical Reasoning

Editors and affiliations

  • Rick Riolo
    • 1
  • Bill Worzel
    • 2
  • Brian Goldman
    • 3
  • Bill Tozier
    • 4
  1. 1.Center for the Study of Complex SysUniversity of MichiganAnn ArborUSA
  2. 2.Evolution EnterprisesAnn ArborUSA
  3. 3.Colorado State UniversityFort CollinsUSA
  4. 4.Ann ArborUSA

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