An Heterogeneous Population-Based Genetic Algorithm for Data Clustering

As a primary data mining method for knowledge discovery, clustering is a technique of classifying a dataset into groups of similar objects. The most popular method for data clustering K-means suffers from the drawbacks of requiring the number of clusters and their initial centers, which should be provided by the user. In the literature, several methods have proposed in a form of k-means variants, genetic algorithms, or combinations between them  for calculating the number of clusters and finding proper clusters centers. However, none of these solutions has provided satisfactory results and determining the number of clusters and the initial centers are still the main challenge in clustering processes. In this paper we present an approach to automatically generate such parameters to achieve optimal clusters using a modified genetic algorithm operating on varied individual structures and using a new crossover operator. Experimental results show that our modified genetic algorithm is a better efficient alternative to the existing approaches

Amélioration de l'algorithme de chauve-souris par modification de règles d’évolution et introduction de mécanisme de croisement

L'algorithme de chauve-souris est l'une des méta-heuristiques prometteuses, récemment proposé pour la résolution de problèmes d'optimisation. Il se base sur la simulation du comportement d'écholocation des chauves-souris. Dans cet article, nous présentons une amélioration de cet algorithme par une modification appropriée des règles d’évolution et l’introduction de mécanisme de croisement. Nous montrons que ce nouvel algorithme améliore les résultats réalisés par l’algorithme de chauve-souris standard et les autres algorithmes le modifiant, publiés récemment. Son application sur cinq fonctions benchmarks largement utilisées dans la littérature, montre de façon claire et précise que les résultats réalisés sont nettement meilleurs.Mots clés : Méta-heuristiques, algorithme de chauve-souris, optimisation, fonction objectif

3D Numerical Simulation of Turbulent Mixed Convection in a Cubical Cavity Containing a Hot Block

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Detection of the protistan parasite, Haplosporidium costale in Crassostrea gigas oysters from the French coast: A retrospective study

International audienceThe parasite Haplosporidium costale is known to infect and cause mortality in the oyster Crassostrea virginica in the USA. Decades after its first description in the 1960s, this parasite was detected in Crassostrea gigas in the USA and China. However, it presented a low prevalence and no mortality was associated with it. More recently, in 2019, H. costale was detected in France in a batch of moribund oysters. In order to observe how long this parasite has been present on French coasts, from Normandy to Thau lagoon, a retrospective investigation was conducted on 871 adult and spat oyster batches from 2004 to 2020. To allow rapid detection on a large panel of samples, a real-time PCR for the H. costale actin gene was developed. This method allowed the detection of H. costale DNA in adults from 2005 and in spat from 2008. The H. costale prevalence in spat appeared higher than in adults over the years studied, 14.59 % compared to 6.50 %, respectively. All samples presenting positive results were then sequenced on two targets, H. costale rRNA and actin genes. The actin gene sequencing highlighted the presence of two H. costale strains. Adult C. gigas as well as spat batches coming from hatcheries and DNA controls from C. virginica all presented with the Profile 1 H. costale strain. The Profile 2 H. costale strain was detected only in C. gigas spat coming from natural sources. These observations suggest a correlation between the origin of oysters and H. costale strains which may have been caused by commercial imports between Japan, USA and France back to the 1970s. Over the positive samples studied, only few batches (n = 3) suffered mortalities which could be hypothesized to be caused by H. costale, all presenting the Profile 1 H. costale strain