Selection Enthusiasm

Agrawal, Abhishek; Mitchell, Ian

doi:10.1007/11903697_57

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Modified Enthusiasm Selection (MES) 16 , is based on standard tournament selection. But MES provides a new opportunity for individuals who are excluded by the tournament selection.…”

Section: Modified Enthusiasm Selectionmentioning

confidence: 99%

“…This new sub-population is added to the current population with elitism replacement. Second, a Modified Enthusiasm Selection 16 , 18 (MES), is used to dynamically adjust the selection pressure. Third, a new mutation operator named Clustering Multi Non Uniform Mutation (CMNUM) with a self adaptive mutation rate is used to find a new peak in new search space.…”

Section: Introductionmentioning

confidence: 99%

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Parameters control in GAs for dynamic optimization

Jebari¹,

Bouroumi²,

Ettouhami³

2013

IJCIS

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The Control of Genetic Algorithms parameters allows to optimize the search process and improves the performance of the algorithm. Moreover it releases the user to dive into a game process of trial and failure to find the optimal parameters. Yet the control of parameters has received much attention in the case of static optimization problems, its investigation in the case of dynamic optimization problems (DOPs) is certainly a promising area of search. Indeed, in the case of DOPs the problem is not just to find the optima but to track the moving optima over time, so the parameters must be adapted to this dynamic environment. The proposed algorithm Parameters Control for Dynamic Optimization (PCDO) is based on Genetic Algorithm with Fitness Sharing (GAFS). To solve DOPs by controlling GAs parameters, PCDO uses several strategies. First, an unsupervised fuzzy clustering method is used to track multiple optimums and to perform GAFS. Second, a modified enthusiasm selection is used to adjust the selection pressure. Third, a clustering multi non uniform Mutation is utilized to locate an unexplored search space. Fourth, a novel technique with multiple crossover is applied to guide the algorithm in promising regions of the search space. Fifth, a self adaptive mutation rate is evolved through generation with a learned parameter, in order to control the diversity of the population. In the concern of maintaining the diversity of the population, a new genetic operator called Fertilization is proposed. PCDO is tested on six problems generated from Generalized Dynamic Benchmark Generator (GDBG). Experimental results demonstrate that PCDO outperforms other GAs on DOPs. Moreover, the ability of PCDO to maintain diversity is demonstrated by a new diversity measure.

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“…Modified Enthusiasm Selection (MES) 16 , is based on standard tournament selection. But MES provides a new opportunity for individuals who are excluded by the tournament selection.…”

Section: Modified Enthusiasm Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parameters control in GAs for dynamic optimization

Jebari¹,

Bouroumi²,

Ettouhami³

2013

IJCIS

View full text Add to dashboard Cite

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Automatic Genetic Fuzzy c-Means

Jebari

Elmoujahid

Ettouhami

2018

Journal of Intelligent Systems

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Abstract Fuzzy c-means is an efficient algorithm that is amply used for data clustering. Nonetheless, when using this algorithm, the designer faces two crucial choices: choosing the optimal number of clusters and initializing the cluster centers. The two choices have a direct impact on the clustering outcome. This paper presents an improved algorithm called automatic genetic fuzzy c-means that evolves the number of clusters and provides the initial centroids. The proposed algorithm uses a genetic algorithm with a new crossover operator, a new mutation operator, and modified tournament selection; further, it defines a new fitness function based on three cluster validity indices. Real data sets are used to demonstrate the effectiveness, in terms of quality, of the proposed algorithm.

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Selection Enthusiasm

Cited by 2 publications

References 6 publications

Parameters control in GAs for dynamic optimization

Parameters control in GAs for dynamic optimization

Automatic Genetic Fuzzy c-Means

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