Paper Info
Title
Enhancing The Efficiency Of Genetic Algorithm By Identifying Linkage Groups Using Dsm Clustering
Abstract
Standard genetic algorithms are not very suited to problems with multivariate interactions among variables. This problem has been identified from the beginning of these algorithms and has been termed as the linkage learning problem. Numerous attempts have been carried out to solve this problem with various degree of success. In this paper, we employ an effective algorithm to cluster a dependency structure matrix (DSM) which can correctly identify the linkage groups. Once all the linkage groups are identified, a simple genetic algorithm using BB-wise crossover can easily solve hard optimization problems. Experimental results with a number of deceptive functions with various sizes presented to show the efficiency enhancement obtained by the proposed method. The results are also compared with Bayesian Optimization Algorithm, a well-known evolutionary optimizer, to demonstrate this improvement.
Year
DOI
Venue
2010
10.1109/CEC.2010.5585936
2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
Keywords
Field
DocType
genetic algorithm,computational modeling,couplings,optimization,clustering algorithms,construction industry,dependency structure matrix,algorithm design and analysis,genetic algorithms,optimization problem
Mathematical optimization,Crossover,Algorithm design,Multivariate statistics,Computer science,Artificial intelligence,Design structure matrix,Cluster analysis,Bayesian optimization algorithm,Optimization problem,Machine learning,Genetic algorithm
Conference
Citations 
PageRank 
References 
3
0.48
11
Authors
4
Name
Order
Citations
PageRank
Amin Nikanjam1628.94
Hadi Sharifi2183.32
B. Hoda Helmi3224.24
Adel Torkaman Rahmani413919.77