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Genetic Programming Theory and Practice IX

By Rick Riolo , Ekaterina Vladislavleva , Jason H. Moore

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This book describes cutting-edge work on genetic programming (GP) theory, applications of GP, and how theory can be used to guide the application of GP. It demonstrates large-scale applications of GP to a variety of problem domains.

Full Description

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  • ISBN13: 978-1-4614-1769-9
  • 290 Pages
  • User Level: Professionals
  • Publication Date: November 2, 2011
  • Available eBook Formats: PDF

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Full Description
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.
Table of Contents

Table of Contents

  1. What’s in an evolved name? The evolution of modularity via tag
  2. based Reference.
  3. Let the Games Evolve!.
  4. Novelty Search and the Problem with Objectives.
  5. A fine
  6. grained view of phenotypes and locality in genetic programming.
  7. Evolution of an Effective Brain
  8. Computer Interface Mouse via Genetic Programming with Adaptive Tarpeian Bloat Control.
  9. Improved Time Series Prediction and Symbolic Regression with Affine Arithmetic.
  10. Computational Complexity Analysis of Genetic Programming – Initial Results and Future Directions.
  11. Accuracy in Symbolic Regression.
  12. Human
  13. Computer Interaction in a Computational Evolution System for the Genetic Analysis of Cancer.
  14. Baseline Genetic Programming: Symbolic Regression on Benchmarks for Sensory Evaluation Modeling.
  15. Detecting Shadow Economy Sizes With Symbolic Regression.
  16. The Importance of Being Flat – Studying the Program Length Distributions of Operator Equalisation.
  17. FFX: Fast, Scalable, Deterministic Symbolic Regression Technology.

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