Skip to main content
SpringerLink
Log in

Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems

  • Book
  • © 2022

Overview

Editors:
  1. Essam Halim Houssein

    1. Faculty of Computers and Information, Minia University, Minia, Egypt

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Mohamed Abd Elaziz

    1. Faculty of Computer Science & Engineering, Galala University, Suze, Egypt

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  3. Diego Oliva

    1. Department of Computer Sciences, University of Guadalajara, Guadalajara, Mexico

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  4. Laith Abualigah

    1. Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, Jordan

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Presents recent research on Integrating Meta-heuristics and Machine Learning for real-world Optimization Problems
  • Brings together outstanding research and recent developments in metaheuristics, Machine learning, and their applications
  • Presented papers describe original works in different topics in science and engineering

Part of the book series: Studies in Computational Intelligence (SCI, volume 1038)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 159.99
Price excludes VAT (USA)
This title has not yet been released. You may pre-order it now and we will ship your order when it is published on 19 Sep 2022.
  • Compact, lightweight edition
  • Free shipping worldwide - see info
Hardcover Book USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (20 chapters)

  1. Front Matter

    Pages i-ix
    Download chapter PDF

  2. Combined Optimization Algorithms for Incorporating DG in Distribution Systems

    • Hussein Abdel-mawgoud, Salah Kamel, Ahmad Eid
    Pages 1-24

  3. Intelligent Computational Models for Cancer Diagnosis: A Comprehensive Review

    • Essam Halim Houssein, Hager N. Hassan, Mustafa M. Al-Sayed, Emad Nabil
    Pages 25-50

  4. Elitist-Ant System Metaheuristic for ITC 2021—Sports Timetabling

    • Ghaith M. Jaradat
    Pages 51-83

  5. Swarm Intelligence Algorithms-Based Machine Learning Framework for Medical Diagnosis: A Comprehensive Review

    • Essam Halim Houssein, Eman Saber, Yaser M. Wazery, Abdelmgeid A. Ali
    Pages 85-106

  6. Aggregation of Semantically Similar News Articles with the Help of Embedding Techniques and Unsupervised Machine Learning Algorithms: A Machine Learning Application with Semantic Technologies

    • Nitesh Tarbani, Kanchan Wadhva
    Pages 107-120

  7. Integration of Machine Learning and Optimization Techniques for Cardiac Health Recognition

    • Essam Halim Houssein, Ibrahim E. Ibrahim, M. Hassaballah, Yaser M. Wazery
    Pages 121-148

  8. Metaheuristics for Parameter Estimation of Solar Photovoltaic Cells: A Comprehensive Review

    • Essam Halim Houssein, Gamela Nageh Zaki, Laith Abualigah, Eman M. G. Younis
    Pages 149-179

  9. Big Data Analysis Using Hybrid Meta-Heuristic Optimization Algorithm and MapReduce Framework

    • Mohammad Qassem Bashabsheh, Laith Abualigah, Mohammad Alshinwan
    Pages 181-223

  10. Deep Neural Network for Virus Mutation Prediction: A Comprehensive Review

    • Takwa Mohamed, Sabah Sayed, Akram Salah, Essam Halim Houssein
    Pages 225-255

  11. 2D Target/Anomaly Detection in Time Series Drone Images Using Deep Few-Shot Learning in Small Training Dataset

    • Mehdi Khoshboresh-Masouleh, Reza Shah-Hosseini
    Pages 257-271

  12. Hybrid Adaptive Moth-Flame Optimizer and Opposition-Based Learning for Training Multilayer Perceptrons

    • Benedict Jun Ma
    Pages 273-319

  13. Early Detection of Coronary Artery Disease Using PSO-Based Neuroevolution Model

    • Mina Karimi, Seyed Mohammad Jafar Jalali, Iman Raeesi Vanani, Diego Oliva
    Pages 321-342

  14. Review for Meta-Heuristic Optimization Propels Machine Learning Computations Execution on Spam Comment Area Under Digital Security Aegis Region

    • Biswajit Mondal, Debkanta Chakraborty, Niloy Kr. Bhattacherjee, Pritam Mukherjee, Sanchari Neogi, Subir Gupta
    Pages 343-361

  15. Solving Reality-Based Trajectory Optimization Problems with Metaheuristic Algorithms Inspired by Metaphors

    • Alfonso Ramos-Michel, Mario A. Navarro, Bernardo Morales-Castañeda, Marco Pérez-Cisneros, Daniel Zaldivar
    Pages 363-397

  16. Parameter Tuning of PID Controller Based on Arithmetic Optimization Algorithm in IOT Systems

    • Mohamed Issa
    Pages 399-417

  17. Testing and Analysis of Predictive Capabilities of Machine Learning Algorithms

    • Ganesh Khekare, Lokesh Kumar Bramhane, Chetan Dhule, Rahul Agrawal, Anil V. Turukmane
    Pages 419-442

  18. AI Based Technologies for Digital and Banking Fraud During Covid-19

    • Mudita Sinha, Elizabeth Chacko, Priya Makhija
    Pages 443-459

  19. Gradient-Based Optimizer for Structural Optimization Problems

    • Mohamed Issa, Yahia Mostafa
    Pages 461-480

  20. Aquila Optimizer Based PSO Swarm Intelligence for IoT Task Scheduling Application in Cloud Computing

    • Laith Abualigah, Mohamed Abd Elaziz, Nima Khodadadi, Agostino Forestiero, Heming Jia, Amir H. Gandomi
    Pages 481-497
Back to top

Keywords

About this book

This book collects different methodologies that permit metaheuristics and machine learning to solve real-world problems. This book has exciting chapters that employ evolutionary and swarm optimization tools combined with machine learning techniques. The fields of applications are from distribution systems until medical diagnosis, and they are also included different surveys and literature reviews that will enrich the reader. Besides, cutting-edge methods such as neuroevolutionary and IoT implementations are presented in some chapters. In this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. 

The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and can be used in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the material canbe helpful for research from the evolutionary computation, artificial intelligence communities.

 

Editors and Affiliations

  • Faculty of Computers and Information, Minia University, Minia, Egypt

    Essam Halim Houssein

  • Faculty of Computer Science & Engineering, Galala University, Suze, Egypt

    Mohamed Abd Elaziz

  • Department of Computer Sciences, University of Guadalajara, Guadalajara, Mexico

    Diego Oliva

  • Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, Jordan

    Laith Abualigah

Bibliographic Information

  • Book Title: Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems

  • Editors: Essam Halim Houssein, Mohamed Abd Elaziz, Diego Oliva, Laith Abualigah

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-030-99079-4

  • Publisher: Springer Cham

  • eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

  • Hardcover ISBN: 978-3-030-99078-7Published: 05 June 2022

  • Softcover ISBN: 978-3-030-99081-7Due: 19 September 2022

  • eBook ISBN: 978-3-030-99079-4Published: 04 June 2022

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: IX, 497

  • Number of Illustrations: 44 b/w illustrations, 183 illustrations in colour

  • Topics: Computational Intelligence, Machine Learning

Publish with us

Policies and ethics

Back to top

Search

Navigation