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Chaotic catfish particle swarm optimization (C-CatfishPSO) is a novel optimization algorithm proposed in this paper. C-CatfishPSO introduces chaotic maps into catfish particle swarm optimization (CatfishPSO), which increase the search capability of CatfishPSO via the chaos approach. Simple CatfishPSO relies on the incorporation of catfish particles into particle swarm optimization (PSO). The introduced...
Catfish particle swarm optimization (CatfishPSO) algorithm is a novel swarm intelligence optimization, which inspired by the behavior between sardines and catfish, i.e. the so-called catfish effect is applied to improve the performance of particle swarm optimization (PSO). In this paper, we propose an improved CatfishPSO with fuzzy adaptive (F-CatfishPSO), which a fuzzy system is implemented to dynamically...
The feature selection process can be considered a problem of global combinatorial optimization in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable classification accuracy. In this study, we propose a combined filter method (ReliefF) and a wrapper method (memetic algorithm, MA) for classification. The goal of our method...
In recent years, many studies have shown that microarray gene expression data is useful for disease identification and cancer classification. However, since gene expression data may contain thousands of genes simultaneously, successful microarray classification can be rather difficult. Feature (gene) selection is a frequently used pre-processing technology for successful classification of microarray...
Many previous research papers have demonstrated that microarray gene expression data are useful for disease classification and medical diagnosis. Cancer microarray data normally have a particular characteristic where features (genes) greatly exceed the instance (tissue sample) numbers. Selecting appropriate numbers and relevant features to differentiate different types of cancer remains a challenge...
Feature selection is a useful pre-processing technique for solving classification problems. The challenge of using evolutionary algorithms lies in solving the feature selection problem caused by the number of features. Classification data may contain useless, redundant or misleading features. To increase the classification accuracy, the primary objective is to remove irrelevant features in the feature...
In order to provide feasible primer set for performing a polymerase chain reaction (PCR) experiment, many primer design methods have been proposed. However, the majority of these methods require a long time to obtain an optimal solution since quantities of template DNA need to be analyzed, and the designed primer sets usually do not provide a specific PCR product size. Evolutionary computation has...
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