Genetic Programming Theory and Practice IV was developed from the fourth workshop at the University of Michigan's Center for the Study of Complex Systems to facilitate the exchange of ideas and information related to the rapidly advancing field of Genetic Programming (GP). Contributions from the foremost international researchers and practitioners in the GP arena examine the similarities and differences between theoretical and empirical results on real-world problems. The text explores the synergy between theory and practice, producing a comprehensive view of the state of the art in GP application. This volume represents a watershed moment in the GP field in that GP has begun to move from hand-crafted software used primarily in academic research, to an engineering methodology applied to commercial applications. It is a unique and indispensable tool for academics, researchers and industry professionals involved in GP, evolutionary computation, machine learning and artificial intelligence. TOC: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.
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