Genetic Algorithms (GAs) is proven to be effective in solving many optimization tasks. GAs is
one of the optimization tools used widely in solving problems based on natural selection and …

Support vector machine (SVM) is a powerful tool for classification and regression problems,
however, its time and space complexities make it unsuitable for large datasets. In this paper …

This study describes an alternative method for deconvolution of overlap** characteristic
Gauss peaks with the help of optimization of a mixed variable genetic algorithm. Continuous …

Many optimization problems have complex search space, which either increase the solving
problem time or finish searching without obtaining the best solution. Genetic Algorithm (GA) …

Understanding the regulation of gene expression is one of the key problems in current
biology. A promising method for that purpose is the determination of the temporal dynamics …

Les progrès technologiques dans les domaines de l'information et des communications ont
permis la modernisation du réseau électrique pour former le réseau électrique intelligent. Le …

Genetic algorithms (GAs) have parallelism among applications of genetic operators to
individuals, but in order to extract high performance of a GPU, parallelizing each genetic …

Screening of water-based Enhanced Oil Recovery (EOR) techniques is a preliminary task in
designing an EOR technique for an oil reservoir. Commonly, a screening table is looked up …

The exploitation of niches by genetic algorithms (GAs) is a computationally expensive, but
effective, methodology for solving complex open problems and real-world applications …

Genetic algorithms (GA) are frequently used to solve scheduling, shortest paths, machine
learning, and modeling problems. Genetic algorithms are basically a search and …