Designing Evolutionary Algorithms for Dy
β Ronald W. Morrison
π Library
π
2004
π Springer
π English
β¦ LIBER β¦
π
Designing Evolutionary Algorithms for Dynamic Environments
β Scribed by Ronald W. Morrison (auth.)
- Publisher
- Springer-Verlag Berlin Heidelberg
- Year
- 2004
- Tongue
- English
- Leaves
- 155
- Series
- Natural Computing Series
- Edition
- 1
- Category
- Library
β¬ Acquire This Volume
No coin nor oath required. For personal study only.
β¦ Synopsis
The robust capability of evolutionary algorithms (EAs) to find solutions to difficult problems has permitted them to become popular as optimization and search techniques for many industries. Despite the success of EAs, the resultant solutions are often fragile and prone to failure when the problem changes, usually requiring human intervention to keep the EA on track. Since many optimization problems in engineering, finance, and information technology require systems that can adapt to changes over time, it is desirable that EAs be able to respond to changes in the environment on their own. This book provides an analysis of what an EA needs to do to automatically and continuously solve dynamic problems, focusing on detecting changes in the problem environment and responding to those changes. In this book we identify and quantify a key attribute needed to improve the detection and response performance of EAs in dynamic environments. We then create an enhanced EA, designed explicitly to exploit this new understanding. This enhanced EA is shown to have superior performance on some types of problems. Our experiments evaluating this enhanced EA indicate some preΒ viously unknown relationships between performance and diversity that may lead to general methods for improving EAs in dynamic environments. Along the way, several other important design issues are addressed involving comΒ putational efficiency, performance measurement, and the testing of EAs in dynamic environments.
β¦ Table of Contents
Front Matter....Pages I-XII
Introduction....Pages 1-12
Problem Analysis....Pages 13-17
Solutions from Nature and Engineering....Pages 19-23
Diversity Measurement....Pages 25-52
A New EA for Dynamic Problems....Pages 53-68
Experimental Methods....Pages 69-84
Performance Measurement....Pages 85-92
Analysis and Interpretation of Experimental Results....Pages 93-122
Experimental Results for Population Initialization....Pages 123-131
Summary and Conclusion....Pages 133-137
Back Matter....Pages 139-149
β¦ Subjects
Logics and Meanings of Programs; Artificial Intelligence (incl. Robotics)
π SIMILAR VOLUMES
Evolutionary Algorithms for Embedded Sys
β Joachim Wegener (auth.), Rolf Drechsler, Nicole Drechsler (eds.)
π Library
π
2003
π Springer US
π English
<p><P><STRONG>Evolutionary Algorithms for Embedded System Design</STRONG> describes how Evolutionary Algorithm (EA) concepts can be applied to circuit and system design - an area where time-to-market demands are critical. EAs create an interesting alternative to other approaches since they can be sc
Evolutionary Optimization in Dynamic Env
β JΓΌrgen Branke (auth.)
π Library
π
2002
π Springer US
π English
<p>Evolutionary Algorithms (EAs) have grown into a mature field of research in optimization, and have proven to be effective and robust problem solvers for a broad range of static real-world optimization problems. Yet, since they are based on the principles of natural evolution, and since natural ev
Evolutionary Algorithms in Molecular Des
β edited by David E. Clark
π Library
π
2000
π Wiley
π English
"This book highlights the versatility of evolutionary algorithms in areas of relevance to molecular design with a particular focus on drug design. The authors, all of whom are experts in their field, discuss the application of these computational methods to a wide range of research problems includ
Evolutionary Algorithms in Engineering D
β David Greiner, AntΓ³nio Gaspar-Cunha, Daniel HernΓ‘ndez-Sosa
π Library
π
2022
π MDPI
π English
<p><span>Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, have allowed us to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly in engineering fields. Their main advantages are the f
Evolutionary Computation in Dynamic and
β Dr. Shengxiang Yang (auth.), Dr. Shengxiang Yang, Dr. Yew-Soon Ong, Dr. Yaochu J
π Library
π
2007
π Springer-Verlag Berlin Heidelberg
π English
<p><P>This book provides a compilation on the state-of-the-art and recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified framework. The motivation for this book arises from the fact that some degree of uncertainty in characterizing any realistic engineerin