Evolutionary machine learning: A survey
Evolutionary Computation (EC) approaches are inspired by nature and solve optimization
problems in a stochastic manner. They can offer a reliable and effective approach to address …
problems in a stochastic manner. They can offer a reliable and effective approach to address …
Survey on evolutionary deep learning: Principles, algorithms, applications, and open issues
Over recent years, there has been a rapid development of deep learning (DL) in both
industry and academia fields. However, finding the optimal hyperparameters of a DL model …
industry and academia fields. However, finding the optimal hyperparameters of a DL model …
COVID-19 image classification using deep features and fractional-order marine predators algorithm
Currently, we witness the severe spread of the pandemic of the new Corona virus, COVID-
19, which causes dangerous symptoms to humans and animals, its complications may lead …
19, which causes dangerous symptoms to humans and animals, its complications may lead …
Adaptive crossover operator based multi-objective binary genetic algorithm for feature selection in classification
Feature selection is a key pre-processing technique for classification which aims at
removing irrelevant or redundant features from a given dataset. Generally speaking, feature …
removing irrelevant or redundant features from a given dataset. Generally speaking, feature …
A survey on evolutionary computation approaches to feature selection
Feature selection is an important task in data mining and machine learning to reduce the
dimensionality of the data and increase the performance of an algorithm, such as a …
dimensionality of the data and increase the performance of an algorithm, such as a …
Novel improved salp swarm algorithm: An application for feature selection
We live in a period when smart devices gather a large amount of data from a variety of
sensors and it is often the case that decisions are taken based on them in a more or less …
sensors and it is often the case that decisions are taken based on them in a more or less …
Binary biogeography-based optimization based SVM-RFE for feature selection
Rapid data growth presents many challenges for Machine Learning (ML) tasks as it can
include lots of irrelevant, noisy, and redundant features. Thus, it is vital to select the most …
include lots of irrelevant, noisy, and redundant features. Thus, it is vital to select the most …
[HTML][HTML] Simpler is better: Lifting interpretability-performance trade-off via automated feature engineering
Abstract Machine learning has proved to generate useful predictive models that can and
should support decision makers in many areas. The availability of tools for AutoML makes it …
should support decision makers in many areas. The availability of tools for AutoML makes it …
Integration of graph clustering with ant colony optimization for feature selection
Feature selection is an important preprocessing step in machine learning and pattern
recognition. The ultimate goal of feature selection is to select a feature subset from the …
recognition. The ultimate goal of feature selection is to select a feature subset from the …
A hybrid genetic algorithm with wrapper-embedded approaches for feature selection
Feature selection is an important research area for big data analysis. In recent years, various
feature selection approaches have been developed, which can be divided into four …
feature selection approaches have been developed, which can be divided into four …