A Micro Genetic Algorithm with Cauchy Mutation for Mechanical Optimization Design Problems

Liu, Zhuangcheng; Lin, Xiaofeng; Shi, Yufeng; Teng, Hong‐Fei

doi:10.3923/itj.2011.1824.1829

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…Haupt and Haupt (2004) explained the history of GA which was developed by John Holland in 1975 over the course of the 1960s and 1970s and finally popularized by his student, David Goldberg, who was able to solve a difficult problem involving the control of gas-pipeline transmission for his dissertation. Liu et al (2011) mentioned some previous studies that implemented GA to solve problems and got the optimal solutions. GA is optimally fit to optimize solution of big data.…”

Section: Genetic Algorithmmentioning

confidence: 99%

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An Application of Genetic Algorithm for Clustering Observations With Incomplete Data

Ananda¹,

Saefuddin

Sartono

2017

IJSA

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Cluster analysis is a method to classify observations into several clusters. A common strategy for clustering the observations uses distance as a similarity index. However distance approach cannot be applied when data is not complete. Genetic Algorithm is applied by involving variance (GACV) in order to solve this problem. This study employed GACV on Iris data that was introduced by Sir Ronald Fisher. Clustering the incomplete data was implemented on data which was produced by deleting some values of Iris data. The algorithm was developed under R 3.0.2 software and got satisfying result for clustering complete data with 95.99% sensitivity and 98% consistency. GACV could be applied to cluster observations with missing value without filling in the missing value or excluding these observations. Performance on clustering incomplete observations is also satisfying but tends to decrease as the proportion of incomplete values increases. The proportion of incomplete values should be less than or equal to 40% to get sensitivity and consistency not less than 90. Keywords: Cluster Analysis, Genetic Algorithm, Incomplete Data.

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Section: Genetic Algorithmmentioning

confidence: 99%

“…Cole (1998) used ranked selection by ranking the individuals based on their fitness value and selected top n select individuals. Liu et al (2011) applied tournament selection by obtaining two individuals randomly and compared their fitness values, the selected individual has a better fitness value. This process is repeated n select times.…”

Section: Selectionmentioning

confidence: 99%