Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms

Zhang, Mengjie; Chung, Henry Shu-Hung; Lo, W.L.

doi:10.1109/tevc.2006.880727

Cited by 262 publications

(113 citation statements)

References 12 publications

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“…GA belongs to a large family of evolutionary computing inspired by natural phenomena and is more reliable technique than any other heuristic mathematical solvers. [26,27]. Due to the simplicity in concept, ease in implementation and less probability of getting stuck in local minima, GA has been used in various applications of array signal processing [28], communication [29] and soft computing [30].…”

Section: Proposed Methodologiesmentioning

confidence: 99%

Amplitude and Directional of Arrival Estimation: Comparison Between Different Techniques

Zaman¹,

Qureshi²,

Naveed³

et al. 2012

PIER B

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Section: Proposed Methodologiesmentioning

confidence: 99%

Amplitude and Directional of Arrival Estimation: Comparison Between Different Techniques

Zaman¹,

Qureshi²,

Naveed³

et al. 2012

PIER B

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“…In [44], clustering analysis was applied to adjust the probabilities of crossover p x and mutation p m in GAs. By applying the k-means algorithm, the population is clustered in each generation and a fuzzy system is used to adjust the values of the genetic operators.…”

Section: Literature Reviewmentioning

confidence: 99%

A Selection Process for Genetic Algorithm Using Clustering Analysis

et al. 2017

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This article presents a newly proposed selection process for genetic algorithms on a class of unconstrained optimization problems. The k-means genetic algorithm selection process (KGA) is composed of four essential stages: clustering, membership phase, fitness scaling and selection. Inspired from the hypothesis that clustering the population helps to preserve a selection pressure throughout the evolution of the population, a membership probability index is assigned to each individual following the clustering phase. Fitness scaling converts the membership scores in a range suitable for the selection function which selects the parents of the next generation. Two versions of the KGA process are presented: using a fixed number of clusters K (KGA f ) and via an optimal partitioning K opt (KGA o ) determined by two different internal validity indices. The performance of each method is tested on seven benchmark problems.

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“…Clustering is divide a data set (pattern) into different classes or clusters according to a particular standard [6][7][8], making the similarity of data within the same cluster as large as possible, and the difference of data not in the same cluster as much as possible. That is to say, clustering is an important technique to explore its intrinsic structure in any given pattern.…”

Section: One-step K-meansmentioning

confidence: 99%

Clustering-Based Differential Evolution with Composite Trial Vector Generation Strategies and Control Parameters

Zhang¹,

Huang²,

Wang³

2016

dtetr

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Abstract. This paper proposed a novel method Clustering-Based Differential Evolution with Composite Trial Vector Generation Strategies and Control Parameters (C-CODE). It combines several effective trial vector generation strategies with some suitable control parameter settings for get rid of the singleness of control parameters and trial vector generation strategies of traditional differential evolution algorithm. And in order to more effective use of population information is hereby joined the one-step k-means clustering algorithm, so as to improve the performance of the algorithm. Finally, the validity and superiority of the algorithm are verified by 13 international standard test functions.

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Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms

Cited by 262 publications

References 12 publications

Amplitude and Directional of Arrival Estimation: Comparison Between Different Techniques

Amplitude and Directional of Arrival Estimation: Comparison Between Different Techniques

A Selection Process for Genetic Algorithm Using Clustering Analysis

Clustering-Based Differential Evolution with Composite Trial Vector Generation Strategies and Control Parameters

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