Paper Info
Title
Adaptive Simulated Annealing Genetic Algorithm For Control Applications
Abstract
We propose an efficient hybrid genetic algorithm named the adaptive simulated annealing genetic algorithm (ASAGA) which is used in control applications. Genetic algorithms are becoming more popular because of their relative simplicity and robustness. Genetic algorithms are global search techniques for nonlinear optimization. However, they are poor at hill-climbing, whereas simulated annealing has the ability of probabilistic hill-climbing. Therefore, combining them produces an adaptive algorithm that has the merits of both genetic algorithms and simulated annealing by introducing an adaptive cooling schedule and mutation operator such as simulated annealing. The validity and efficiency of the proposed algorithm are illustrated by simulation examples for system identification and control that include neural networks which are particularly suitable for applications of ASAGA.
Year
DOI
Venue
1996
10.1080/00207729608929210
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
Field
DocType
Volume
Simulated annealing,Mathematical optimization,Computer science,Robustness (computer science),Adaptive simulated annealing,Adaptive algorithm,Probabilistic logic,Artificial neural network,System identification,Genetic algorithm
Journal
27
Issue
ISSN
Citations 
2
0020-7721
4
PageRank 
References 
Authors
0.73
9
2
Name
Order
Citations
PageRank
Il-Kwon Jeong1164.65
Ju-Jang Lee253967.24