Genetic Programming Theory and Practice VII

ISBN-13: 9781441916259 / Angielski / Twarda / 2009 / 231 str.

Rick Riolo; Una-May O'Reilly; Trent McConaghy

Opis

Genetic programming has emerged as an important computational methodology for solving complex problems in a diversity of disciplines. In an effort to foster collaborations and facilitate the exchange of ideas and information related to the rapidly advancing field of Genetic Programming, the annual Genetic Programming Theory and Practice Workshop was organized by the University of Michigan's Center for the Study of Complex Systems to provide a forum for both those who develop computational theory and those that practice the art of computation. Genetic Programming Theory and Practice VII presents the results of this 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
• Contributed by GP theorists from major universities and active practitioners from industry examining how GP theory informs practice and how GP practice impacts GP theory

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.

Spis treści

GPTP 2009: An Example of Evolvability.- Environmental Sensing of Expert Knowledge in a Computational Evolution System for Complex Problem Solving in Human Genetics.- Evolving Coevolutionary Classifiers under large Attribute Spaces.- Symbolic Regression via Genetic Programming as a Discovery Engine: Insights on Outliers and Prototypes.- Symbolic Regression of Implicit Equations.- A Steady-State Version of the Age-Layered Population Structure EA.- Latent Variable Symbolic Regression for High-Dimensional Inputs.- Algorithmic Trading with Developmental and Linear Genetic Programming.- High-significance Averages of Event-Related Potential via Genetic Programming.- Using Multi-objective Genetic Programming to Synthesize Stochastic Processes.- Graph Structured Program Evolution: Evolution of Loop Structures.- A Functional Crossover Operator for Genetic Programming.- Symbolic Regression of Conditional Target Expressions.- Index.-