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.
