Evolutionary Machine Learning Techniques

Algorithms and Applications

  • Seyedali Mirjalili
  • Hossam Faris
  • Ibrahim Aljarah

Part of the Algorithms for Intelligent Systems book series (AIS)

Table of contents

  1. Front Matter
    Pages i-x
  2. Seyedali Mirjalili, Hossam Faris, Ibrahim Aljarah
    Pages 1-7
  3. Classification and Predication

    1. Front Matter
      Pages 9-9
    2. Ala’ M. Al-Zoubi, Ali Asghar Heidari, Maria Habib, Hossam Faris, Ibrahim Aljarah, Mohammad A. Hassonah
      Pages 11-34
    3. Seyed Hamed Hashemi Mehne, Seyedali Mirjalili
      Pages 35-50
    4. Ali Asghar Heidari, Yingyu Yin, Majdi Mafarja, Seyed Mohammad Jafar Jalali, Jin Song Dong, Seyedali Mirjalili
      Pages 51-66
    5. Seyed Mohammad Jafar Jalali, Rachid Hedjam, Abbas Khosravi, Ali Asghar Heidari, Seyedali Mirjalili, Saeid Nahavandi
      Pages 67-83
    6. Rawan I. Yaghi, Hossam Faris, Ibrahim Aljarah, Ala’ M. Al-Zoubi, Ali Asghar Heidari, Seyedali Mirjalili
      Pages 85-111
    7. Rana Faris, Bara’a Almasri, Hossam Faris, Faris M. AL-Oqla, Doraid Dalalah
      Pages 113-127
  4. Feature Selection

    1. Front Matter
      Pages 129-129
    2. Ruba Abu Khurma, Ibrahim Aljarah, Ahmad Sharieh, Seyedali Mirjalili
      Pages 131-173
    3. Maria Habib, Ibrahim Aljarah, Hossam Faris, Seyedali Mirjalili
      Pages 203-229
    4. Feras Namous, Hossam Faris, Ali Asghar Heidari, Monther Khalafat, Rami S. Alkhawaldeh, Nazeeh Ghatasheh
      Pages 231-250
    5. Thaer Thaher, Ali Asghar Heidari, Majdi Mafarja, Jin Song Dong, Seyedali Mirjalili
      Pages 251-272
    6. Qasem Al-Tashi, Helmi Md Rais, Said Jadid Abdulkadir, Seyedali Mirjalili, Hitham Alhussian
      Pages 273-286

About this book


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.


Artificial Neural Network Probabilistic Neural Network Self-Optimizing Neural Network Feedforward Neural Network Radial Basis Function Network Recurrent Neural Network Spiking Neural Network Neuro-fuzzy Networks Modular Neural Network Physical Neural Network

Editors and affiliations

  • Seyedali Mirjalili
    • 1
  • Hossam Faris
    • 2
  • Ibrahim Aljarah
    • 3
  1. 1.Torrens University AustraliaBrisbaneAustralia
  2. 2.King Abdullah II School for Information TechnologyThe University of JordanAmmanJordan
  3. 3.King Abdullah II School for Information TechnologyThe University of JordanAmmanJordan

Bibliographic information