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Computer Science - Artificial Intelligence | Genetic Programming Theory and Practice IX

Genetic Programming Theory and Practice IX

Riolo, Rick, Vladislavleva, Ekaterina, Moore, Jason H. (Eds.)

2011, XXVIII, 264 p.

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  • 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
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.

Content Level » Professional/practitioner

Keywords » FFX - automatic programming - computational complexity - evolutionary games - genetic programming - novelty search - program induction - sensory evaluation modeling - symbolic regression - tag-based reference - time series prediction

Related subjects » Artificial Intelligence - Software Engineering - Theoretical Computer Science

Table of contents 

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

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