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.
Author(s): Lee Spector, Kyle Harrington, Brian Martin (auth.), Rick Riolo, Ekaterina Vladislavleva, Jason H. Moore (eds.)
Series: Genetic and Evolutionary Computation
Edition: 1
Publisher: Springer-Verlag New York
Year: 2011
Language: English
Pages: 264
Tags: Artificial Intelligence (incl. Robotics); Theory of Computation; Algorithm Analysis and Problem Complexity; Programming Techniques
Front Matter....Pages i-xxvii
What’s in an Evolved Name? The Evolution of Modularity via Tag-Based Reference....Pages 1-16
Let the Games Evolve!....Pages 17-36
Novelty Search and the Problem with Objectives....Pages 37-56
A Fine-Grained View of Phenotypes and Locality in Genetic Programming....Pages 57-76
Evolution of an Effective Brain-Computer Interface Mouse via Genetic Programming with Adaptive Tarpeian Bloat Control....Pages 77-95
Improved Time Series Prediction and Symbolic Regression with Affine Arithmetic....Pages 97-112
Computational Complexity Analysis of Genetic Programming - Initial Results and Future Directions....Pages 113-128
Accuracy in Symbolic Regression....Pages 129-151
Human-Computer Interaction in a Computational Evolution System for the Genetic Analysis of Cancer....Pages 153-171
Baseline Genetic Programming: Symbolic Regression on Benchmarks for Sensory Evaluation Modeling....Pages 173-194
Detecting Shadow Economy Sizes with Symbolic Regression....Pages 195-210
The Importance of Being Flat–Studying the Program Length Distributions of Operator Equalisation....Pages 211-233
FFX: Fast, Scalable, Deterministic Symbolic Regression Technology....Pages 235-260
Back Matter....Pages 261-263
