A Dimensionality reduced Text data clustering with prediction of optimal number of clusters

Murty, M. Ramakrishna; Murthy, J. V. R.; Reddy, Prasad; Satapathy, Suresh Chandra

doi:10.5958/j.0975-8070.2.2.010

Cited by 7 publications

(2 citation statements)

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“…Genetic Algorithm is an evolutionary search-based optimization technique which is based on Darwinian's theory of "Survival of The Fittest". Genetic algorithms are often reviewed as function optimizer [10], although the range of problems to which Genetic algorithms have been applied are quite broad [3], [4]. It performs search from a population of points rather than from a single point thus accelerating the search process.…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…Let us assume p1= p2=1. i.e.. child1 is formed as: par1 [1][2][3][4][5][6][7][8][9][10]:par2 [11][12][13][14][15][16][17][18][19][20] and child2 is formed as: par2 [1][2][3][4][5][6][7][8][9][10]:par2 [11][12][13][14][15][16][17][18][19][20] But volume of 1s in par [1][2][3][4][5][6][7][8][9][10] is not equal to volume of 1s in par2 [1][2][3][4][5]…”

Section: Working Of Crossovermentioning

confidence: 99%

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Performance Comparison between Genetic Algorithm and Population-Based Incremental Learning Algorithm using Student Ranking Application

Viswanathsarma¹,

Bhattacharyya

Kim

2017

IJBSBT

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This paper compares the performance of Genetic Algorithm and Population-based Incremental Learning algorithm on a student ranking application. The proposed student ranking application focuses on proving the students' achievements in fields like attendance, achievements and backlogs apart from academics. This application produces the output according to the prioritized weights inputted by the user. Genetic Algorithm and Population-based Incremental Learning is evolutionary search based optimization algorithm which can arrive at near-optimal solution faster by using randomized techniques. These are stochastic search based algorithms that include randomness to escape local optima. The two algorithms are compared based on the accuracy of optimal value, number of generations and time taken to reach optimal value.

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Section: Genetic Algorithmmentioning

confidence: 99%