Genetic Programming Theory and Practice VII

By: Rick Riolo (Editor), Una-May O'Reilly (Editor), Trent McConaghy (Editor)

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Hardcover | 24 November 2009

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Hardcover
248 Pages

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23.5 x 16.51 x 1.91

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Genetic Programming Theory and Practice VII presents the results of the annual Genetic Programming Theory and Practice Workshop, contributed by the foremost international researchers and practitioners in the GP arena. Contributions examine the similarities and differences between theoretical and empirical results on real-world problems, and explore the synergy between theory and practice, producing a comprehensive view of the state of the art in GP application. Application areas include chemical process control, circuit design, financial data mining and bio-informatics, to name a few.

About this book: Discusses the hurdles encountered when solving large-scale, cutting-edge applications, provides in-depth presentations of the latest and most significant applications of GP and the most recent theoretical results with direct applicability to state-of-the-art problems. Genetic Programming Theory and Practice VII is suitable for researchers, practitioners and students of Genetic Programming, including industry technical staffs, technical consultants and business entrepreneurs.

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Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Computer Programming & Software Development Algorithms & Data Structures

Contributing Authors	p. vii
Preface	p. xi
Foreword	p. xiii
GPTP 2009: An Example of Evolvability	p. 1
Environmental Sensing of Expert Knowledge in a Computational Evolution System for Complex Problem Solving in Human Genetics	p. 19
Evolving Coevolutionary Classifiers under large Attribute Spaces	p. 37
Symbolic Regression via Genetic Programming as a Discovery Engine: Insights on Outliers and Prototypes	p. 55
Symbolic Regression of Implicit Equations	p. 73
A Steady-State Version of the Age-Layered Population Structure EA	p. 87
Latent Variable Symbolic Regression for High-Dimensional Inputs	p. 103
Algorithmic Trading with Developmental and Linear Genetic Programming	p. 119
High-significance Averages of Event-Related Potential via Genetic Programming	p. 135
Using Multi-objective Genetic Programming to Synthesize Stochastic Processes	p. 159
Graph Structured Program Evolution: Evolution of Loop Structures	p. 177
A Functional Crossover Operator for Genetic Programming	p. 195
Symbolic Regression of Conditional Target Expressions	p. 211
Index	p. 229
Table of Contents provided by Ingram. All Rights Reserved.

Genetic Programming Theory and Practice VII

At a Glance

Hardcover

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