This book provides an in-depth analysis of the current evolutionary
machine learning techniques. Discussing the most highly regarded
methods for classification, 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-optimizing neural
networks, radial basis function networks, recurrent neural
networks, spiking neural networks, neuro-fuzzy networks, modular
neural networks, physical neural networks, and deep neural
networks. The book provides essential definitions, literature
reviews, and the training algorithms for machine learning using
classical and modern nature-inspired techniques. It also
investigates the pros and cons of classical training algorithms. It
features a range of proven and recent nature-inspired algorithms
used to train different types of artificial neural networks,
including genetic algorithm, ant colony optimization, particle
swarm optimization, grey wolf optimizer, whale optimization
algorithm, ant lion optimizer, moth flame algorithm, dragonfly
algorithm, salp swarm algorithm, multi-verse optimizer, and sine
cosine algorithm. The book also covers applications of the improved
artificial neural networks to solve classification, clustering,
prediction and regression problems in diverse fields.
General
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