A new grouping genetic algorithm for clustering problems

Agustin-Blas, L.E.; Salcedo‐Sanz, Sancho; Jiménez-Fernández, Silvia; Carro-Calvo, L.; Ser, Javier Del; Portilla-Figueras, Antonio

doi:10.1016/j.eswa.2012.02.149

Cited by 127 publications

(50 citation statements)

References 41 publications

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“…In this algorithm [7] for exit from trap of local optimal in k-means algorithm we use of genetics optimization algorithm for better data clustering. Because of evolutionary algorithm, such as genetics, have ability for global search in answer, use of them for clustering, decrease probability from placing answer of algorithm in local optimal.…”

Section: Genetic Clustering Algorithmmentioning

confidence: 99%

Extended PSO Algorithm for Improvement Problems K-Means Clustering Algorithm

Lashkari¹,

Rostami²,

Branch³

2014

IJMIT

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Section: Genetic Clustering Algorithmmentioning

confidence: 99%

Extended PSO Algorithm for Improvement Problems K-Means Clustering Algorithm

Lashkari¹,

Rostami²,

Branch³

2014

IJMIT

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“…Note that, for a given problem instance, the length of the elements section is fixed, whereas the length of the groups section is not. The GE has been applied in many real world grouping problems including data clustering (Agustín-Blas et al, 2012) and machine-part cell formation (Brown and Sumichrast, 2003). A useful aspect of the GE representation is its ability to address the underlying problem of constructing fit subgroups (with potentially reduced cost).…”

Section: Grouping Representationsmentioning

confidence: 99%

A grouping hyper-heuristic framework: Application on graph colouring

Elhag¹,

Özcan²

2015

Expert Systems with Applications

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Grouping problems are hard to solve combinatorial optimisation problems which require partitioning of objects into a minimum number of subsets while a given objective is simultaneously optimized. Selection hyper-heuristics are high level general purpose search methodologies that operate on a space formed by a set of low level heuristics rather than solutions. Most of the recently proposed selection hyper-heuristics are iterative and make use of two key methods which are employed successively; heuristic selection and move acceptance. At each step, a new solution is produced after a selected heuristic is applied to the solution in hand and then the move acceptance method is used to decide whether the resultant solution replaces the current one or not. In this study, we present a selection hyper-heuristic framework including a fixed set of low level heuristics specifically designed for grouping problems. The performance of different hyperheuristics using different components within the framework is investigated on a representative grouping problem of graph colouring. Additionally, the hyper-heuristic performing the best on graph colouring is applied to a benchmark of examination timetabling instances. The empirical results shows that the proposed grouping hyper-heuristic is not only sufficiently general, but also able to achieve high quality solutions for graph colouring and examination timetabling.

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“…In GGA algorithm, two different mutation and crossover operators namely Mutation by cluster splitting and Mutation by cluster merging are described. For more details, refer to [2].…”

Section: Gga Algorithmmentioning

confidence: 99%

“…The Grouping Genetic Algorithm [2](GGA) 10 , is one class of evolutionary algorithms which is modified specifically to cope with grouping problems i.e. the problems that in them some items should be assigned to a set of predefined groups.…”

Section: Gga Algorithmmentioning

confidence: 99%

Introduce A New Algorithm For Data Clustering By Genetic Algorithm

Vahidi

Mirpour²,

Technical³

2014

J. Math. Computer Sci.

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Clustering of data into adequate categories is one of the most important issues in pattern recognition. What is important in clustering, doing so is no predetermined pattern, provided that the same data should be in a category. In this paper, first, a clustering method using a grouping genetic algorithm (GGA) to describe, then the proposed model we introduce and the proposed method are tested on several sets of data and finally we compare the proposed method with the GGA algorithm.The results show that the proposed algorithm is well-GGA gives us the answer and in terms of time and space complexity are much better than GGA.

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A new grouping genetic algorithm for clustering problems

Cited by 127 publications

References 41 publications

Extended PSO Algorithm for Improvement Problems K-Means Clustering Algorithm

Extended PSO Algorithm for Improvement Problems K-Means Clustering Algorithm

A grouping hyper-heuristic framework: Application on graph colouring

Introduce A New Algorithm For Data Clustering By Genetic Algorithm

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