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A Survey on Evolutionary Computation Approaches to Feature Selection
TLDR
This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms.
Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach
TLDR
The experimental results show that the two PSO-based multi-objective algorithms can automatically evolve a set of nondominated solutions and the first algorithm outperforms the two conventional methods, the single objective method, and the two-stage algorithm.
Evolving Deep Convolutional Neural Networks for Image Classification
TLDR
A new method using genetic algorithms for evolving the architectures and connection weight initialization values of a deep convolutional neural network to address image classification problems and a novel fitness evaluation method is proposed to speed up the heuristic search with substantially less computational resource.
Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification
TLDR
This article proposes an automatic CNN architecture design method by using genetic algorithms, to effectively address the image classification tasks and shows the very comparable classification accuracy to the best one from manually designed and automatic + manually tuning CNNs, while consuming fewer computational resources.
Evolving Deep Convolutional Neural Networks by Variable-Length Particle Swarm Optimization for Image Classification
TLDR
This paper focuses on utilising Particle Swarm Optimisation to automatically search for the optimal architecture of CNNs without any manual work involved and proposes a novel encoding strategy inspired by computer networks which empowers particle vectors to easily encode CNN layers.
Binary particle swarm optimisation for feature selection: A filter based approach
TLDR
The results show that with proper weights, two proposed algorithms can significantly reduce the number of features and achieve similar or even higher classification accuracy in almost all cases.
A New Representation in PSO for Discretization-Based Feature Selection
TLDR
A new method called potential particle swarm optimization (PPSO) is proposed which employs a new representation that can reduce the search space of the problem and a new fitness function to better evaluate candidate solutions to guide the search.
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