Evolutionary Algorithms are very powerful techniques used to find solutions to real-world search and optimization problems. They are artificial intelligence techniques which mimic nature according to Darwin's principal of the "survival of the fittest" in order to explore and explode large search space and find near-global optima. In this book, a large spectrum of innovative evolutionary and intelligence methods are presented and used for solving various application problems, name and among others: Genetic Algorithms, Evolutions Strategies or Constrained Optimization, Genetic Programming, Sampling Methods in Evolutionary Computation, Tabu Methods, Metamodelassisted Evolutionary Algorithms, Multi Objective Robust Design, Hierarchical Asynchronous Parallel Evolutionary Algorithms, Micro Distributed Genetic Algorithms and Hybrid Optimization. The reader will easily access the comprehensive methods described in the fourteen chapters that work impressively well on practical problems representative of real engineering situations in the areas of Control, Electronics, Civil Engineering, Aerospace Engineering, Turbomachinery and Medical Engineering and Energy.
This collective book, written by internationally recognized experts in the fields of evolutionary design optimization, will therefore be of significant interest and value to computer scientists, researchers and post graduate students, and practicing senior or young engineers involved in complex design Optimization problems.
Auflage
Sprache
Verlagsort
Southampton
Großbritannien
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Editions-Typ
Maße
Höhe: 230 mm
Breite: 155 mm
ISBN-13
978-1-84564-038-5 (9781845640385)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Part I: Theoretical Aspects Using Evolution Strategies to Solve Constrained Optimization Problems; Evolutionary Algorithms in Engineering Optimization; Sampling Methods in Evolutionary Computation: Assuring Genetic Diversity and Stochastic Selection Part II: Numerical Aspects A Fast and Robust Adaptive Methodology for Design under Uncertainties Based On Dace Response Surface and Game Theory; Hybrid Optimization with Automatic Switching Among Optimization Algorithms; Cost-Effective Metamodel-Assisted Evolutionary Algorithms; A Rough Set Based Tabu Enhanced Genetic Algorithm Approach to Rule Induction; Usage of Approximation Techniques in Evolutionary Algorithms with Application Examples to Aerodynamic Shape Design Problems. Part III: Applications Aspects Micro Distribute Genetic Algorithms in Structural Shape, System Identification and Biological Optimization problems; Multi-Objective Robust Design Optimization Using IOSO Technology Algorithms; Economic Dispatch Optimizations In Electric Power Systems by a Flexible Evolution Agent; Evolutionary Optimization Tools for Multi Objective Design in Aerospace Engineering: from Theory to MDO Applications; Human-Competitive Automated Engineering Design and Optimization By Means Of Genetic Programming; Single and Multi-Objective Optimization in Civil Engineering