Genetic Programming Theory and Practice IX

1. Front Matter

   Pages i-xxvii

   PDF

2. What’s in an Evolved Name? The Evolution of Modularity via Tag-Based Reference

   • Lee Spector, Kyle Harrington, Brian Martin, Thomas Helmuth

   Pages 1-16

3. Let the Games Evolve!

   • Moshe Sipper

   Pages 17-36

4. Novelty Search and the Problem with Objectives

   • Joel Lehman, Kenneth O. Stanley

   Pages 37-56

5. A Fine-Grained View of Phenotypes and Locality in Genetic Programming

   • James McDermott, Edgar Galván-Lopéz, Michael O’Neill

   Pages 57-76

6. Evolution of an Effective Brain-Computer Interface Mouse via Genetic Programming with Adaptive Tarpeian Bloat Control

   • Riccardo Poli, Mathew Salvaris, Caterina Cinel

   Pages 77-95

7. Improved Time Series Prediction and Symbolic Regression with Affine Arithmetic

   • Cassio Pennachin, Moshe Looks, J. A. de Vasconcelos

   Pages 97-112

8. Computational Complexity Analysis of Genetic Programming - Initial Results and Future Directions

   • Frank Neumann, Una-May O’Reilly, Markus Wagner

   Pages 113-128

9. Accuracy in Symbolic Regression

   • Michael F. Korns

   Pages 129-151

10. Human-Computer Interaction in a Computational Evolution System for the Genetic Analysis of Cancer

    • Jason H. Moore, Douglas P. Hill, Jonathan M. Fisher, Nicole Lavender, La Creis Kidd

    Pages 153-171

11. Baseline Genetic Programming: Symbolic Regression on Benchmarks for Sensory Evaluation Modeling

    • Pierre-Luc Noel, Kalyan Veeramachaneni, Una-May O’Reilly

    Pages 173-194

12. Detecting Shadow Economy Sizes with Symbolic Regression

    • Philip D. Truscott, Michael F. Korns

    Pages 195-210

13. The Importance of Being Flat–Studying the Program Length Distributions of Operator Equalisation

    • Sara Silva, Leonardo Vanneschi

    Pages 211-233

14. FFX: Fast, Scalable, Deterministic Symbolic Regression Technology

    • Trent McConaghy

    Pages 235-260

15. Back Matter

    Pages 261-263

    PDF

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 include: modularity and scalability; evolvability; human-competitive results; the need for important high-impact GP-solvable problems;; the risks of search stagnation and of cutting off paths to solutions; the need for novelty; empowering GP search with expert knowledge; In addition, GP symbolic regression is thoroughly discussed, addressing such topics as guaranteed reproducibility of SR; validating SR results, measuring and controlling genotypic complexity; controlling phenotypic complexity; identifying, monitoring, and avoiding over-fitting; finding a comprehensive collection of SR benchmarks, comparing SR to machine learning. This text is for all GP explorers. 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.

• FFX
• automatic programming
• computational complexity
• evolutionary games
• genetic programming
• novelty search
• program induction
• sensory evaluation modeling
• symbolic regression
• tag-based reference
• time series prediction
• algorithm analysis and problem complexity

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

  Rick Riolo

• Evolved Analytics Europe BVBA, Wijnegem, Belgium

  Ekaterina Vladislavleva

• , Institute for Quantitative, Dartmouth Medical School, Lebanon, USA

  Jason H. Moore

Policies and ethics