Opposition Based Genetic Algorithm with Cauchy Mutation for Function Optimization

Evolutionary algorithms (EA) have been used in data classification and data clustering task since the advent of these algorithms. Nonlinear complex optimization problems have been the area of interest since very long time. The EA have been applied successfully on these optimization problems. The evo...

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Bibliographic Details
Published in2010 International Conference on Information Science and Applications pp. 1 - 7
Main Authors Iqbal, M Amjad, Khan, Naveed Kazim, Jaffar, M Arfan, Ramzan, M, Baig, A Rauf
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2010
Subjects
Ant colony optimization
Clustering algorithms
Convergence
Evolutionary computation
Genetic algorithms
Genetic mutations
Learning
Neural networks
Robustness
Testing
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Summary:Evolutionary algorithms (EA) have been used in data classification and data clustering task since the advent of these algorithms. Nonlinear complex optimization problems have been the area of interest since very long time. The EA have been applied successfully on these optimization problems. The evolutionary algorithms suffer a lot due to their slow convergence rate, mainly due to evolutionary nature of these algorithms. This paper presents a new mutation scheme for opposition based genetic algorithms (OGA-CM). This scheme tunes the population during evolutionary process effectively by using Cauchy Mutation (CM). The performance of the algorithm is tested over suit of 5 functions. Opposition based Genetic Algorithm (OGA) is used as competitor algorithm to compare the results of the proposed algorithm. The results show that the proposed method outperforms GA and OGA for most of the test functions.
ISBN:9781424459421
1424459427
1424459419
9781424459414
ISSN:2162-9048
DOI:10.1109/ICISA.2010.5480382