Multi-variant differential evolution algorithm for feature selection.This work introduces a new population-based stochastic search technique, named multi-variant differential evolution (MVDE) algorithm for solving fifteen well-known real world problems from UCI repository and compared to four popular optimization methods.  The MVDE proposes a new self-adaptive scaling factor based on cosine and logistic distributions as an almost factor-free optimization technique.  In this paper, a new feature selection algorithm based on the MVDE method and artificial neural network is presented which enabled MVDE to get a combination features' set, accelerate the accuracy of the classification, and optimize both the structure and weights of Artificial Neural Network (ANN) simultaneously. 
MVDE
Multi-variant differential evolution algorithm for feature selection.This work introduces a new population-based stochastic search technique, named multi-variant differential evolution (MVDE) algorithm for solving fifteen well-known real world problems from UCI repository and compared to four popular optimization methods.  The MVDE proposes a new self-adaptive scaling factor based on cosine and logistic distributions as an almost factor-free optimization technique.  In this paper, a new feature selection algorithm based on the MVDE method and artificial neural network is presented which enabled MVDE to get a combination features' set, accelerate the accuracy of the classification, and optimize both the structure and weights of Artificial Neural Network (ANN) simultaneously. 
MVDE
Multi-variant differential evolution algorithm for feature selection.This work introduces a new population-based stochastic search technique, named multi-variant differential evolution (MVDE) algorithm for solving fifteen well-known real world problems from UCI repository and compared to four popular optimization methods.  The MVDE proposes a new self-adaptive scaling factor based on cosine and logistic distributions as an almost factor-free optimization technique.  In this paper, a new feature selection algorithm based on the MVDE method and artificial neural network is presented which enabled MVDE to get a combination features' set, accelerate the accuracy of the classification, and optimize both the structure and weights of Artificial Neural Network (ANN) simultaneously. 
MVDE
Multi-variant differential evolution algorithm for feature selection.This work introduces a new population-based stochastic search technique, named multi-variant differential evolution (MVDE) algorithm for solving fifteen well-known real world problems from UCI repository and compared to four popular optimization methods.  The MVDE proposes a new self-adaptive scaling factor based on cosine and logistic distributions as an almost factor-free optimization technique.  In this paper, a new feature selection algorithm based on the MVDE method and artificial neural network is presented which enabled MVDE to get a combination features' set, accelerate the accuracy of the classification, and optimize both the structure and weights of Artificial Neural Network (ANN) simultaneously. 
MVDE
Multi-variant differential evolution algorithm for feature selection.This work introduces a new population-based stochastic search technique, named multi-variant differential evolution (MVDE) algorithm for solving fifteen well-known real world problems from UCI repository and compared to four popular optimization methods.  The MVDE proposes a new self-adaptive scaling factor based on cosine and logistic distributions as an almost factor-free optimization technique.  In this paper, a new feature selection algorithm based on the MVDE method and artificial neural network is presented which enabled MVDE to get a combination features' set, accelerate the accuracy of the classification, and optimize both the structure and weights of Artificial Neural Network (ANN) simultaneously. 
MVDE