Buch, Englisch, 231 Seiten, Format (B × H): 164 mm x 242 mm, Gewicht: 1160 g
Buch, Englisch, 231 Seiten, Format (B × H): 164 mm x 242 mm, Gewicht: 1160 g
Reihe: Genetic and Evolutionary Computation
ISBN: 978-1-4419-1625-9
Verlag: Springer Us
Genetic Programming Theory and Practice VII presents the results of the annual Genetic Programming Theory and Practice 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. 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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
GPTP 2009: An Example of Evolvability.- Environmental Sensing of Expert Knowledge in a Computational Evolution System for Complex Problem Solving in Human Genetics.- Evolving Coevolutionary Classifiers Under Large Attribute Spaces.- Symbolic Regression Via Genetic Programming as a Discovery Engine: Insights on Outliers and Prototypes.- Symbolic Regression of Implicit Equations.- A Steady-State Version of the Age-Layered Population Structure EA.- Latent Variable Symbolic Regression for High-Dimensional Inputs.- Algorithmic Trading with Developmental and Linear Genetic Programming.- High-Significance Averages of Event-Related Potential Via Genetic Programming.- Using Multi-Objective Genetic Programming to Synthesize Stochastic Processes.- Graph Structured Program Evolution: Evolution of Loop Structures.- A Functional Crossover Operator for Genetic Programming.- Symbolic Regression of Conditional Target Expressions.
