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Handbook of Whale Optimization Algorithm: Variants, Hybrids, Improvements, and Applications provides the most in-depth look at an emerging meta-heuristic that has been widely used in both science and industry. Whale Optimization Algorithm has been cited more than 5000 times in Google Scholar, thus solving optimization problems using this algorithm requires addressing a number of challenges including multiple objectives, constraints, binary decision variables, large-scale search space, dynamic objective function, and noisy parameters to name a few. This handbook provides readers with in-depth analysis of this algorithm and existing methods in the literature to cope with such challenges. The authors and editors also propose several improvements, variants and hybrids of this algorithm. Several applications are also covered to demonstrate the applicability of methods in this book. Provides in-depth analysis of equations, mathematical models and mechanisms of the Whale Optimization Algorithm Proposes different variants of the Whale Optimization Algorithm to solve binary, multiobjective, noisy, dynamic and combinatorial optimization problems Demonstrates how to design, develop and test different hybrids of Whale Optimization Algorithm Introduces several application areas of the Whale Optimization Algorithm, focusing on sustainability Includes source code from applications and algorithms that is available online

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.

This book introduces readers to the fundamentals of artificial neural networks, with a special emphasis on evolutionary algorithms. At first, the book offers a literature review of several well-regarded evolutionary algorithms, including particle swarm and ant colony optimization, genetic algorithms and biogeography-based optimization. It then proposes evolutionary version of several types of neural networks such as feed forward neural networks, radial basis function networks, as well as recurrent neural networks and multi-later perceptron. Most of the challenges that have to be addressed when training artificial neural networks using evolutionary algorithms are discussed in detail. The book also demonstrates the application of the proposed algorithms for several purposes such as classification, clustering, approximation, and prediction problems. It provides a tutorial on how to design, adapt, and evaluate artificial neural networks as well, and includes source codes for most of the proposed techniques as supplementary materials.