Titlebook: Evolutionary Machine Learning Techniques; Algorithms and Appli Seyedali Mirjalili,Hossam Faris,Ibrahim Aljarah Book 2020 Springer Nature Si

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Book 2020sification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimi

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https://doi.org/10.1007/978-3-642-99339-8-dominating sorting genetic algorithm (NSGA-II), common traditional machine learning algorithms, and some conventional filter-based feature selection methods. As per the obtained results, MOPSO is competitive and outperforms NSGA-II, traditional machine learning methods, and filter-based methods in most of the studied datasets.

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Support Vector Machine: Applications and Improvements Using Evolutionary Algorithmsr. The method has been applied to a set of experimental data for diabetes mellitus diagnosis. Results show that the method leads to a classifier which distinguished healthy and patient cases with 87.5% of accuracy.