Genetic Programming Theory and Practice XVIII

1. Front Matter

   Pages i-xiv

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2. Finding Simple Solutions to Multi-Task Visual Reinforcement Learning Problems with Tangled Program Graphs

   • Caleidgh Bayer, Ryan Amaral, Robert J. Smith, Alexandru Ianta, Malcolm I. Heywood

   Pages 1-19

3. Grammar-Based Vectorial Genetic Programming for Symbolic Regression

   • Philipp Fleck, Stephan Winkler, Michael Kommenda, Michael Affenzeller

   Pages 21-43

4. Grammatical Evolution Mapping for Semantically-Constrained Genetic Programming

   • Alcides Fonseca, Paulo Santos, Guilherme Espada, Sara Silva

   Pages 45-62

5. What Can Phylogenetic Metrics Tell us About Useful Diversity in Evolutionary Algorithms?

   • Jose Guadalupe Hernandez, Alexander Lalejini, Emily Dolson

   Pages 63-82

6. An Exploration of Exploration: Measuring the Ability of Lexicase Selection to Find Obscure Pathways to Optimality

   • Jose Guadalupe Hernandez, Alexander Lalejini, Charles Ofria

   Pages 83-107

7. Feature Discovery with Deep Learning Algebra Networks

   • Michael F. Korns

   Pages 109-127

8. Back to the Future—Revisiting OrdinalGP and Trustable Models After a Decade

   • Mark Kotanchek, Nathan Haut

   Pages 129-142

9. Fitness First

   • W. B. Langdon

   Pages 143-164

10. Designing Multiple ANNs with Evolutionary Development: Activity Dependence

    • Julian Francis Miller

    Pages 165-180

11. Evolving and Analyzing Modularity with GLEAM (Genetic Learning by Extraction and Absorption of Modules)

    • Anil Kumar Saini, Lee Spector

    Pages 181-195

12. Evolution of the Semiconductor Industry, and the Start of X Law

    • Andrew N. Sloss

    Pages 197-209

13. Back Matter

    Pages 211-212

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This book, written by the foremost international researchers and practitioners of genetic programming (GP), explores 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 book 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

• Department of Computer Science and Engineering, Michigan State University, East Lansing, USA

  Wolfgang Banzhaf

• Tecnológico Nacional de México/IT de Tijuana, Tijuana, Mexico

  Leonardo Trujillo

• School of Informatics, Communications and Media, University of Applied Sciences Upper Austria, Hagenberg, Austria

  Stephan Winkler

• Evolution Enterprise, Ann Arbor, USA

  Bill Worzel

Wolfgang Banzhaf is a professor in the Department of Computer Science and Engineering at Michigan State University.

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