Genetic Programming Theory and Practice IX
(Sprache: Englisch)
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...
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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.
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.
Klappentext zu „Genetic Programming Theory and Practice IX “
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.
Inhaltsverzeichnis zu „Genetic Programming Theory and Practice IX “
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.
Bibliographische Angaben
- 2011, 2011, 264 Seiten, mit Abbildungen, Maße: 16 x 24,1 cm, Gebunden, Englisch
- Herausgegeben: Rick Riolo, Ekaterina Vladislavleva, Jason H. Moore
- Verlag: Springer, Berlin
- ISBN-10: 1461417694
- ISBN-13: 9781461417699
- Erscheinungsdatum: 01.11.2011
Sprache:
Englisch
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