Abstract
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. For more updated chances, this factor is binary-mapped by incorporating an adaptive crossover operator. During the evolution, both greedy and less-greedy variants are managed by adjusting and incorporating the binary scaling factor and elite identification mechanism into a new multi-mutation crossover process through a number of sequentially evolutionary phases. Feature selection decreases the number of features by eliminating irrelevant or misleading, noisy and redundant data which can accelerate the process of classification. 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. The experimental results show the encouraging behavior of the proposed algorithm in terms of the classification accuracies and optimal number of feature selection.
Highlights
A feature selection approach was proposed based on two different implementation of multi-objective Artificial Bee Colony optimization (ABC) algorithm combined with non-dominated sorting procedure and genetic operators for examining twelve benchmark d atasets24. In25, a new method called Particle Swarm Optimization (PSO)-DFS using bare-bone particle swarm optimization (BBPSO) for discretization and feature selection in a single stage was proposed for solving ten highdimensional datasets. In26, a comprehensive study to investigate the use of Genetic programming (GP) for feature construction and selection on high-dimensional classification problems was presented and tested on seven high-dimensional gene expression problems
Datasets with different issues of instances and attributes are chosen for validating multi-variant differential evolution (MVDE)
This work presents a novel optimization algorithm (MVDE), which has multi variant mutation with adaptive scaling factor is developed by integrating adaptive crossover rate with mutation factors and adaptive selection of parent to achieve better performance
Summary
Chen et al proposed two novel Bacterial Foraging Optimization algorithms (BFO), which named Adaptive Chemotaxis Bacterial Foraging Optimization algorithm (ACBFO) and Improved Swarming and Elimination-Dispersal Bacterial Foraging Optimization algorithm (ISEDBFO) to create the mapping relationship between the bacterium and the feature subset and to evaluate the importance of features This method dealt with feature selection problems and tested ten public datasets of UCI27. Majdi et al proposed a Grasshopper Optimization Algorithm (GOA) as a search strategy to design a wrapper-based feature selection method in the form of four different strategies to moderate the immature convergence and stagnation drawbacks of the conventional GOA These approaches were benchmarked on twenty-two public UCI datasets[28]
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