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Genetic Programming Theory and Practice IX

  • Book
  • © 2011

Overview

Editors:
  1. Rick Riolo

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

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    You can also search for this editor in PubMed   Google Scholar

  2. Ekaterina Vladislavleva

    1. Evolved Analytics Europe BVBA, Wijnegem, Belgium

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    You can also search for this editor in PubMed   Google Scholar

  3. Jason H. Moore

    1. , Institute for Quantitative, Dartmouth Medical School, Lebanon, USA

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    You can also search for this editor in PubMed   Google Scholar

  • Describes cutting-edge work on genetic programming (GP) theory, applications of GP, and how theory can be used to guide application of GP Demonstrates large-scale applications of GP to a variety of problem domains
  • Reveals an inspiring synergy between GP applications and the latest in theoretical results for state-of –the-art problem solving
  • Addresses symbolic regression as a mode of genetic programming

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

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Table of contents (13 chapters)

  1. Front Matter

    Pages i-xxvii
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  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
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Keywords

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 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.

Editors and Affiliations

  • , 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

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