Genetic Programming Theory and Practice XIII

1. Front Matter

   Pages i-xx

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2. Evolving Simple Symbolic Regression Models by Multi-Objective Genetic Programming

   • Michael Kommenda, Gabriel Kronberger, Michael Affenzeller, Stephan M. Winkler, Bogdan Burlacu

   Pages 1-19

3. Learning Heuristics for Mining RNA Sequence-Structure Motifs

   • Achiya Elyasaf, Pavel Vaks, Nimrod Milo, Moshe Sipper, Michal Ziv-Ukelson

   Pages 21-38

4. Kaizen Programming for Feature Construction for Classification

   • Vinícius Veloso de Melo, Wolfgang Banzhaf

   Pages 39-57

5. GP As If You Meant It: An Exercise for Mindful Practice

   • William A. Tozier

   Pages 59-78

6. nPool: Massively Distributed Simultaneous Evolution and Cross-Validation in EC-Star

   • Babak Hodjat, Hormoz Shahrzad

   Pages 79-90

7. Highly Accurate Symbolic Regression with Noisy Training Data

   • Michael F. Korns

   Pages 91-115

8. Using Genetic Programming for Data Science: Lessons Learned

   • Steven Gustafson, Ram Narasimhan, Ravi Palla, Aisha Yousuf

   Pages 117-135

9. The Evolution of Everything (EvE) and Genetic Programming

   • W. P. Worzel

   Pages 137-149

10. Lexicase Selection for Program Synthesis: A Diversity Analysis

    • Thomas Helmuth, Nicholas Freitag McPhee, Lee Spector

    Pages 151-167

11. Behavioral Program Synthesis: Insights and Prospects

    • Krzysztof Krawiec, Jerry Swan, Una-May O’Reilly

    Pages 169-183

12. Using Graph Databases to Explore the Dynamics of Genetic Programming Runs

    • Nicholas Freitag McPhee, David Donatucci, Thomas Helmuth

    Pages 185-201

13. Predicting Product Choice with Symbolic Regression and Classification

    • Philip Truscott, Michael F. Korns

    Pages 203-217

14. Multiclass Classification Through Multidimensional Clustering

    • Sara Silva, Luis Muñoz, Leonardo Trujillo, Vijay Ingalalli, Mauro Castelli, Leonardo Vanneschi

    Pages 219-239

15. Prime-Time: Symbolic Regression Takes Its Place in the Real World

    • Sean Stijven, Ekaterina Vladislavleva, Arthur Kordon, Lander Willem, Mark E. Kotanchek

    Pages 241-260

16. Back Matter

    Pages 261-262

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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: multi-objective genetic programming, learning heuristics, Kaizen programming, Evolution of Everything (EvE), lexicase selection, behavioral program synthesis, symbolic regression with noisy training data, graph databases, and multidimensional clustering. It also covers several chapters on best practices and lesson learned from hands-on experience. Additional application areas include financial operations, genetic analysis, and predicting product choice. 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.

• Feature generation
• Genetic programming
• Symbolic regression
• Machine learning
• Evolutionary algorithms
• Semantic programming
• Geometric programming
• Big data
• Cloud computing
• Data science
• Hyper heuristics
• Multi-objective optimization
• Lexicase selection
• Singularity
• algorithm analysis and problem complexity

• Center for the Study of Complex Systems, University of Michigan, Ann Arbor, USA

  Rick Riolo

• Evolution Enterprises, Ann Arbor, USA

  W.P. Worzel

• Evolved Analytics, Midland, USA

  Mark Kotanchek

• Evolved Analytics LLC, Midland, USA

  Arthur Kordon

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