Genetic programming theory and practice VII

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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.

Author(s): Una-May O’Reilly, Trent McConaghy (auth.), Rick Riolo, Una-May O'Reilly, Trent McConaghy (eds.)
Series: Genetic and Evolutionary Computation
Edition: 1
Publisher: Springer US
Year: 2010

Language: English
Pages: 231
City: New York
Tags: Computing Methodologies; Artificial Intelligence (incl. Robotics); Theory of Computation; Algorithm Analysis and Problem Complexity; Programming Techniques

Front Matter....Pages 1-13
GPTP 2009: An Example of Evolvability....Pages 1-18
Environmental Sensing of Expert Knowledge in a Computational Evolution System for Complex Problem Solving in Human Genetics....Pages 19-36
Evolving Coevolutionary Classifiers Under Large Attribute Spaces....Pages 37-54
Symbolic Regression Via Genetic Programming as a Discovery Engine: Insights on Outliers and Prototypes....Pages 55-72
Symbolic Regression of Implicit Equations....Pages 73-85
A Steady-State Version of the Age-Layered Population Structure EA....Pages 87-102
Latent Variable Symbolic Regression for High-Dimensional Inputs....Pages 103-118
Algorithmic Trading with Developmental and Linear Genetic Programming....Pages 119-134
High-Significance Averages of Event-Related Potential Via Genetic Programming....Pages 135-157
Using Multi-Objective Genetic Programming to Synthesize Stochastic Processes....Pages 159-175
Graph Structured Program Evolution: Evolution of Loop Structures....Pages 177-194
A Functional Crossover Operator for Genetic Programming....Pages 195-210
Symbolic Regression of Conditional Target Expressions....Pages 211-228
Back Matter....Pages 1-3