A grouping genetic algorithm for the microcell sectorization problem

Brown, Evelyn C.; Vroblefski, Mark

doi:10.1016/j.engappai.2004.08.003

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…In the GGA, the encoding, crossover and mutation operators of traditional GAs are modified to be applied in grouping problems with a high performance. The GGA has been successfully applied to a number of problems in different fields such as telecommunications [18], manufacturing [19], and industrial engineering [20,21,22,23].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Grouping Genetic Algorithm for Data Clustering and Big Data Analysis

Razavi¹,

Ebadati

Asadi

et al. 2015

Adaptation, Learning, and Optimization

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Abstract. Clustering as a formal, systematic subject in dissertations can be considered the most influential unsupervised learning problem; so, as every other problem of this kind, it deals with finding the structure in a collection of unlabeled data. One of the matters associated with this subject is undoubtedly determination of the number of clusters. In this chapter, an efficient grouping genetic algorithm is proposed under the circumstances of an anonymous number of clusters. Concurrent clustering with different number of clusters is implemented on the same data in each chromosome of grouping genetic algorithm in order to discern the accurate number of clusters. In subsequent iterations of the algorithm, new solutions with different clusters number or distinct accuracy of clustering are produced by application of efficient crossover and mutation operators that led to significant improvement of clustering. Furthermore, a local search by a special probability is applied in each chromosome of each new population in order to increase the accuracy of clustering.These special operators will lead to the successful application of the proposed method in the big data analysis. To prove the accuracy and the efficiency of the algorithm, its tested on various artificial and real data sets in a comparable manner. Most of the datasets consisted of overlapping clusters, but the algorithm could detect the proper number of all data sets with high accuracy of clustering. The consequences make the best evidence of the algorithms successful performance of finding an appropriate number of clusters and accomplishment of the best clusterings quality in comparison with others.

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Section: Introductionmentioning

confidence: 99%

An Efficient Grouping Genetic Algorithm for Data Clustering and Big Data Analysis

Razavi¹,

Ebadati

Asadi

et al. 2015

Adaptation, Learning, and Optimization

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A Hybrid Grouping Genetic Algorithm for citywide ubiquitous WiFi access deployment

Agustin-Blas

Salcedo‐Sanz

Vidales

et al. 2009

2009 IEEE Congress on Evolutionary Computation

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An Island Grouping Genetic Algorithm for Fuzzy Partitioning Problems

Salcedo‐Sanz

Ser²,

Geem

2014

The Scientific World Journal

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This paper presents a novel fuzzy clustering technique based on grouping genetic algorithms (GGAs), which are a class of evolutionary algorithms especially modified to tackle grouping problems. Our approach hinges on a GGA devised for fuzzy clustering by means of a novel encoding of individuals (containing elements and clusters sections), a new fitness function (a superior modification of the Davies Bouldin index), specially tailored crossover and mutation operators, and the use of a scheme based on a local search and a parallelization process, inspired from an island-based model of evolution. The overall performance of our approach has been assessed over a number of synthetic and real fuzzy clustering problems with different objective functions and distance measures, from which it is concluded that the proposed approach shows excellent performance in all cases.

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A grouping genetic algorithm for the microcell sectorization problem

Cited by 3 publications

References 13 publications

An Efficient Grouping Genetic Algorithm for Data Clustering and Big Data Analysis

An Efficient Grouping Genetic Algorithm for Data Clustering and Big Data Analysis

A Hybrid Grouping Genetic Algorithm for citywide ubiquitous WiFi access deployment

An Island Grouping Genetic Algorithm for Fuzzy Partitioning Problems

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