Genetic Programming Theory and Practice IV

- Introduction

- Genome-wide genetic analysis using genetic programming: the critical need for expert knowledge

- Lifting the Curse of Dimensionality

- Boosting improves stability and accuracy of genetic programming in biological sequence classification

- Othogonal Evoluton of Teams: A class of algorithms for evolving teams with inversely correlated errors

- Multidimensional tags, cooperative populations, and genetic programming

- Accelerating GP by Co-evolving Fast Fitness Predictors

- Multi-Domain Observations Concerning the Use of Genetic Programming to Automatically Synthesize Human-Competitive Designs for Analog Circuits, Optical Lens Systems, Controllers, Antennas, Mechanical Systems, and Quantum Computing Circuits

- Robust Pareto Front GP Parameter Selection Based on Design of Experiments and Industrial Data

- Pursuing the Pareto Paradigm

- Applying Genetic Programming to Reservoir History Matching Problem

- Comparison of Robustness of Three Filter Design Strategies Using Genetic Programming and Bond Graphs

- Design of posynomial models for mosfets: Symbolic Regression using Genetic Algorithms

- Phase Transitions in Genetic Programming Search

- Efficient Markov chain model of machine code program execution and halting

- A re-examination of a real world blood flow modeling problem using context aware crossover

- Stock Selection - An Innovative Application of Genetic Programming Methodology

- Large-Scale, Time-Constrained Symbolic Regression

- Index.

From the reviews:"Every cutting-edge researcher, in every computational discipline, working on any real-world application, should make it a point to keep abreast of the ongoing progress of genetic programming theory and practice, which is currently available in this book. ... a great win-win synergy opportunity here for less-cutting-edge researchers to try these maturing tools on more intuitive data masses; they should be more able to appreciate the results, and the genetic programming cryptography community might then learn something new about how to interpret post-scientific results." (Chaim Scheff, ACM Computing Reviews, Vol. 49 (8), August, 2008)