Rick Riolo / Terence Soule / Bill Worzel (eds.)

Genetic Programming Theory and Practice IV

Herausgegeben:Riolo, Rick; Soule, Terence; Worzel, Bill

• Gebundenes Buch

Produktbeschreibung

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.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Genetic and Evolutionary Computation
• Verlag: Springer / Springer US / Springer, Berlin
• Artikelnr. des Verlages: 11618119, 978-0-387-33375-5
• 2007 edition
• Seitenzahl: 338
• Erscheinungstermin: 14. März 2007
• Englisch
• Abmessung: 237mm x 165mm x 32mm
• Gewicht: 685g
• ISBN-13: 9780387333755
• ISBN-10: 0387333754
• Artikelnr.: 20923376

Autorenporträt

Rick Riolo, University of Michigan, Ann Arbor, MI, USA / Terence Soule, University of Idaho, Moscow, ID, USA / Bill Worzel, Genetics Squared, Ann Arbor, MI, USA

Inhaltsangabe

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.

Rezensionen

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)