Every real-world problem from economic to scientific and engineering fields is ultimately confronted with a common task, viz., optimization. Genetic and evolutionary algorithms (GEAs) have often achieved an enviable success in solving optimization problems in a wide range of disciplines. The goal of
Advances in Evolutionary Algorithms: Theory, Design and Practice
โ Scribed by Dr. Chang Wook Ahn (auth.)
- Publisher
- Springer-Verlag Berlin Heidelberg
- Year
- 2006
- Tongue
- English
- Leaves
- 179
- Series
- Studies in Computational Intelligence 18
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
Genetic and evolutionary algorithms (GEAs) have often achieved an enviable success in solving optimization problems in a wide range of disciplines. The goal of this book is to provide effective optimization algorithms for solving a broad class of problems quickly, accurately, and reliably by employing evolutionary mechanisms. In this regard, five significant issues have been investigated:
- Bridging the gap between theory and practice of GEAs, thereby providing practical design guidelines.
- Demonstrating the practical use of the suggested road map.
- Offering a useful tool to significantly enhance the exploratory power in time-constrained and memory-limited applications.
- Providing a class of promising procedures that are capable of scalably solving hard problems in the continuous domain.
- Opening an important track for multiobjective GEA research that relies on decomposition principle.
This book serves to play a decisive role in bringing forth a paradigm shift in future evolutionary computation.
โฆ Table of Contents
Introduction....Pages 1-5
Practical Genetic Algorithms....Pages 7-22
Real-World Application: Routing Problem....Pages 23-43
Elitist Compact Genetic Algorithms....Pages 45-83
Real-coded Bayesian Optimization Algorithm....Pages 85-124
Multiobjective Real-coded Bayesian Optimization Algorithm....Pages 125-151
Conclusions....Pages 153-157
โฆ Subjects
Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics)
๐ SIMILAR VOLUMES
Every real-world problem from economic to scientific and engineering fields is ultimately confronted with a common task, viz., optimization. Genetic and evolutionary algorithms (GEAs) have often achieved an enviable success in solving optimization problems in a wide range of disciplines. The goal of
In comparing this book with, say Goldberg's "Genetic Algorithms..." (may be the most popular genetic algorithms text), this book reads more like a German habilitation thesis (which I imagine it may have served as such), where as Goldberg's book seems more of a light introduction for the mathematical