Полное описание

Genetic Programming Theory and Practice IX : сборник / editor.: R. Riolo [et al.] ; edited by Rick Riolo, Ekaterina Vladislavleva, Jason H. Moore. - New York, NY : Springer, 2011. - on-line. - (Genetic and Evolutionary Computation). - URL: http://dx.doi.org/10.1007/978-1-4614-1770-5. - Загл. с экрана. - ISBN 978-1-4614-1770-5. - Текст : электронный.
Содержание:
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.

ГРНТИ	УДК
50.05.13	004.42

Рубрики:
computer science
computer programming
computers
algorithms
artificial intelligence
computer Science
artificial Intelligence (incl Robotics)
theory of Computation
algorithm Analysis and Problem Complexity
programming Techniques

Аннотация: 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.
Доп. точки доступа:
Riolo, R.\editor.\
Vladislavleva, E.\editor.\
Moore, J.\editor.\

http://dx.doi.org/10.1007/978-1-4614-1770-5

Держатели документа:
Государственная публичная научно-техническая библиотека России : 123298, г. Москва, ул. 3-я Хорошевская, д. 17 (Шифр в БД-источнике (KATBW): -546546859)

Шифр в сводном ЭК: 6474b79484069d44f8023d9c94fac767

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