<p><P>In recent years, the issue of linkage in GEAs has garnered greater attention and recognition from researchers. Conventional approaches that rely much on ad hoc tweaking of parameters to control the search by balancing the level of exploitation and exploration are grossly inadequate. As shown i
Linkage in Evolutionary Computation
β Scribed by Josef Schwarz, Jiri Jaros (auth.), Ying-ping Chen, Meng-Hiot Lim (eds.)
- Publisher
- Springer-Verlag Berlin Heidelberg
- Year
- 2008
- Tongue
- English
- Leaves
- 486
- Series
- Studies in Computational Intelligence 157
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
In recent years, the issue of linkage in GEAs has garnered greater attention and recognition from researchers. Conventional approaches that rely much on ad hoc tweaking of parameters to control the search by balancing the level of exploitation and exploration are grossly inadequate. As shown in the work reported here, such parameters tweaking based approaches have their limits; they can be easily βfooledβ by cases of triviality or peculiarity of the class of problems that the algorithms are designed to handle. Furthermore, these approaches are usually blind to the interactions between the decision variables, thereby disrupting the partial solutions that are being built up along the way.
The whole volume consisting of 19 chapters is divided into 3 parts: Models and Theories; Operators and Frameworks; Applications. This edited volume will serve as a useful guide and reference for researchers who are currently working in the area of linkage. For postgraduate research students, this volume will serve as a good source of reference. It is also suitable as a text for a graduate level course focusing on linkage issues. For practitioners who are looking at putting into practice the concept of linkage, the few chapters on applications will serve as a useful guide.
β¦ Table of Contents
Front Matter....Pages -
Front Matter....Pages 1-1
Parallel Bivariate Marginal Distribution Algorithm with Probability Model Migration....Pages 3-23
Linkages Detection in Histogram-Based Estimation of Distribution Algorithm....Pages 25-40
Linkage in Island Models....Pages 41-60
Real-Coded ECGA for Solving Decomposable Real-Valued Optimization Problems....Pages 61-86
Linkage Learning Accuracy in the Bayesian Optimization Algorithm....Pages 87-107
The Impact of Exact Probabilistic Learning Algorithms in EDAs Based on Bayesian Networks....Pages 109-139
Linkage Learning in Estimation of Distribution Algorithms....Pages 141-156
Front Matter....Pages 157-157
Parallel GEAs with Linkage Analysis over Grid....Pages 159-187
Identification and Exploitation of Linkage by Means of Alternative Splicing....Pages 189-223
A Clustering-Based Approach for Linkage Learning Applied to Multimodal Optimization....Pages 225-248
Studying the Effects of Dual Coding on the Adaptation of Representation for Linkage in Evolutionary Algorithms....Pages 249-284
Symbiotic Evolution to Avoid Linkage Problem....Pages 285-314
EpiSwarm, a Swarm-Based System for Investigating Genetic Epistasis....Pages 315-334
Real-Coded Extended Compact Genetic Algorithm Based on Mixtures of Models....Pages 335-358
Front Matter....Pages 359-359
Genetic Algorithms for the Airport Gate Assignment: Linkage, Representation and Uniform Crossover....Pages 361-387
A Decomposed Approach for the Minimum Interference Frequency Assignment....Pages 389-417
Set Representation and Multi-parent Learning within an Evolutionary Algorithm for Optimal Design of Trusses....Pages 419-439
A Network Design Problem by a GA with Linkage Identification and Recombination for Overlapping Building Blocks....Pages 441-459
Knowledge-Based Evolutionary Linkage in MEMS Design Synthesis....Pages 461-483
Back Matter....Pages -
β¦ Subjects
Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics)
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