Genetic Programming Theory and Practice IV
Springer-Verlag New York Inc.
978-0-387-33375-5 (ISBN)
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.
Genetic Programming: Theory and Practice.- Genome-Wide Genetic Analysis Using Genetic Programming: The Critical Need for Expert Knowledge.- Lifting the Curse of Dimensionality.- Genetic Programming for Classifying Cancer Data and Controlling Humanoid Robots.- Boosting Improves Stability and Accuracy of Genetic Programming in Biological Sequence Classification.- Orthogonal Evolution of Teams: A Class of Algorithms for Evolving Teams with Inversely Correlated Errors.- Multidimensional Tags, Cooperative Populations, and Genetic Programming.- Coevolving Fitness Models for Accelerating Evolution and Reducing Evaluations.- 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 Genetic Programming Parameter Selection Based on Design of Experiments and Industrial Data.- Pursuing the Pareto Paradigm: Tournaments, Algorithm Variations and Ordinal Optimization.- 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.- Large-Scale, Time-Constrained Symbolic Regression.- Stock Selection: An Innovative Application of Genetic Programming Methodology.
Reihe/Serie | Genetic and Evolutionary Computation |
---|---|
Zusatzinfo | 200 Illustrations, black and white; XVI, 338 p. 200 illus. |
Verlagsort | New York, NY |
Sprache | englisch |
Maße | 155 x 235 mm |
Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
ISBN-10 | 0-387-33375-4 / 0387333754 |
ISBN-13 | 978-0-387-33375-5 / 9780387333755 |
Zustand | Neuware |
Haben Sie eine Frage zum Produkt? |
aus dem Bereich