Evolutionary Machine Learning Techniques: Algorithms and Applications (Algorithms for Intelligent Systems)

"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." - Prové de l'editor Front Matter ....Pages i-x Introduction to Evolutionary Machine Learning Techniques (Seyedali Mirjalili, Hossam Faris, Ibrahim Aljarah)....Pages 1-7 Front Matter ....Pages 9-9 Salp Chain-Based Optimization of Support Vector Machines and Feature Weighting for Medical Diagnostic Information Systems (Ala’ M. Al-Zoubi, Ali Asghar Heidari, Maria Habib, Hossam Faris, Ibrahim Aljarah, Mohammad A. Hassonah)....Pages 11-34 Support Vector Machine: Applications and Improvements Using Evolutionary Algorithms (Seyed Hamed Hashemi Mehne, Seyedali Mirjalili)....Pages 35-50 Efficient Moth-Flame-Based Neuroevolution Models (Ali Asghar Heidari, Yingyu Yin, Majdi Mafarja, Seyed Mohammad Jafar Jalali, Jin Song Dong, Seyedali Mirjalili)....Pages 51-66 Autonomous Robot Navigation Using Moth-Flame-Based Neuroevolution (Seyed Mohammad Jafar Jalali, Rachid Hedjam, Abbas Khosravi, Ali Asghar Heidari, Seyedali Mirjalili, Saeid Nahavandi)....Pages 67-83 Link Prediction Using Evolutionary Neural Network Models (Rawan I. Yaghi, Hossam Faris, Ibrahim Aljarah, Ala’ M. Al-Zoubi, Ali Asghar Heidari, Seyedali Mirjalili)....Pages 85-111 Evolving Genetic Programming Models for Predicting Quantities of Adhesive Wear in Low and Medium Carbon Steel (Rana Faris, Bara’a Almasri, Hossam Faris, Faris M. AL-Oqla, Doraid Dalalah)....Pages 113-127 Front Matter ....Pages 129-129 EvoloPy-FS: An Open-Source Nature-Inspired Optimization Framework in Python for Feature Selection (Ruba Abu Khurma, Ibrahim Aljarah, Ahmad Sharieh, Seyedali Mirjalili)....Pages 131-173 Multi-objective Particle Swarm Optimization: Theory, Literature Review, and Application in Feature Selection for Medical Diagnosis (Maria Habib, Ibrahim Aljarah, Hossam Faris, Seyedali Mirjalili)....Pages 175-201 Multi-objective Particle Swarm Optimization for Botnet Detection in Internet of Things (Maria Habib, Ibrahim Aljarah, Hossam Faris, Seyedali Mirjalili)....Pages 203-229 Evolutionary and Swarm-Based Feature Selection for Imbalanced Data Classification (Feras Namous, Hossam Faris, Ali Asghar Heidari, Monther Khalafat, Rami S. Alkhawaldeh, Nazeeh Ghatasheh)....Pages 231-250 Binary Harris Hawks Optimizer for High-Dimensional, Low Sample Size Feature Selection (Thaer Thaher, Ali Asghar Heidari, Majdi Mafarja, Jin Song Dong, Seyedali Mirjalili)....Pages 251-272 A Review of Grey Wolf Optimizer-Based Feature Selection Methods for Classification (Qasem Al-Tashi, Helmi Md Rais, Said Jadid Abdulkadir, Seyedali Mirjalili, Hitham Alhussian)....Pages 273-286