Buch, Englisch, 800 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 1223 g
Buch, Englisch, 800 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 1223 g
Reihe: Genetic and Evolutionary Computation
ISBN: 978-1-4899-9460-8
Verlag: Springer US
This textbook is a second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly expanded and adapted for the classroom. The various features of multi-objective evolutionary algorithms are presented here in an innovative and student-friendly fashion, incorporating state-of-the-art research. The book disseminates the application of evolutionary algorithm techniques to a variety of practical problems. It contains exhaustive appendices, index and bibliography and links to a complete set of teaching tutorials, exercises and solutions.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
- Mathematik | Informatik Mathematik Operations Research Spieltheorie
- Mathematik | Informatik EDV | Informatik Informatik Logik, formale Sprachen, Automaten
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmierung: Methoden und Allgemeines
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
Weitere Infos & Material
Basic Concepts.- MOP Evolutionary Algorithm Approaches.- MOEA Local Search and Coevolution.- MOEA Test Suites.- MOEA Testing and Analysis.- MOEA Theory and Issues.- Applications.- MOEA Parallelization.- Multi-Criteria Decision Making.- Alternative Metaheuristics.
