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Metaheuristics in Machine Learning: Theory and Applications

  • Book
  • © 2021

Overview

Editors:
  1. Diego Oliva

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

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  2. Essam H. Houssein

    1. Department of Computer Science, Faculty of Computers and Information, Minia University, Minia, Egypt

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  3. Salvador Hinojosa

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

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  • Provides representative tools used for machine learning and metaheuristic algorithms
  • Focuses on the theory and application of metaheuristic algorithms in machine learning, including hybridization and implementations in different fields
  • Is self-explained and explains the used algorithm, the selected problem, and the implementation
  • Offers practical examples, comparisons, and experimental results
  • Presents topics which are selected based on their importance and complexity in the field, for example, biochemistry, image processing, clustering, feature selection, energy, among others

Part of the book series: Studies in Computational Intelligence (SCI, volume 967)

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Table of contents (31 chapters)

  1. Front Matter

    Pages i-xiv
    Download chapter PDF

  2. Cross Entropy Based Thresholding Segmentation of Magnetic Resonance Prostatic Images Using Metaheuristic Algorithms

    • Omar Zárate, Daniel Záldivar
    Pages 1-35

  3. Hyperparameter Optimization in a Convolutional Neural Network Using Metaheuristic Algorithms

    • Angel Gaspar, Diego Oliva, Erik Cuevas, Daniel Zaldívar, Marco Pérez, Gonzalo Pajares
    Pages 37-59

  4. Diagnosis of Collateral Effects in Climate Change Through the Identification of Leaf Damage Using a Novel Heuristics and Machine Learning Framework

    • Juan Salazar, Eddy Sánchez-De La Cruz, Alberto Ochoa-Zezzatti, Martin Montes, Roberto Contreras-Masse, José Mejia
    Pages 61-75

  5. Feature Engineering for Machine Learning and Deep Learning Assisted Wireless Communication

    • Vijay Kumar, Sarat Kumar Patra
    Pages 77-95

  6. Genetic Operators and Their Impact on the Training of Deep Neural Networks

    • David Eliel Bocanegra Michel, Daniel Zaldivar Navarro
    Pages 97-124

  7. Implementation of Metaheuristics with Extreme Learning Machines

    • Hector Escobar, Erik Cuevas
    Pages 125-147

  8. Architecture Optimization of Convolutional Neural Networks by Micro Genetic Algorithms

    • Edgar Saul Marquez Casillas, Valentín Osuna-Enciso
    Pages 149-167

  9. Optimising Connection Weights in Neural Networks Using a Memetic Algorithm Incorporating Chaos Theory

    • Seyed Jalaleddin Mousavirad, Gerald Schaefer, Hossein Ebrahimpour-Komleh
    Pages 169-192

  10. A Review of Metaheuristic Optimization Algorithms in Wireless Sensor Networks

    • Essam H. Houssein, Mohammed R. Saad, Kashif Hussain, Hassan Shaban, M. Hassaballah
    Pages 193-217

  11. A Metaheuristic Algorithm for Classification of White Blood Cells in Healthcare Informatics

    • Ana Carolina Borges Monteiro, Yuzo Iano, Reinaldo Padilha França, Rangel Arthur
    Pages 219-238

  12. Multi-level Thresholding Image Segmentation Based on Nature-Inspired Optimization Algorithms: A Comprehensive Review

    • Essam H. Houssein, Bahaa El-din Helmy, Diego Oliva, Ahmed A. Elngar, Hassan Shaban
    Pages 239-265

  13. Hybrid Harris Hawks Optimization with Differential Evolution for Data Clustering

    • Laith Abualigah, Mohamed Abd Elaziz, Mohammad Shehab, Osama Ahmad Alomari, Mohammad Alshinwan, Hamzeh Alabool et al.
    Pages 267-299

  14. Variable Mesh Optimization for Continuous Optimization and Multimodal Problems

    • Jarvin A. Antón-Vargas, Luis A. Quintero-Domínguez, Guillermo Sosa-Gómez, Omar Rojas
    Pages 301-317

  15. Traffic Control Using Image Processing and Deep Learning Techniques

    • Rafael Baroni, Sthefanie Premebida, Marcella Martins, Diego Oliva, Erikson Freitas de Morais, Max Santos
    Pages 319-335

  16. Drug Design and Discovery: Theory, Applications, Open Issues and Challenges

    • Essam H. Houssein, Mosa E. Hosney, Diego Oliva, No Ortega-Sánchez, Waleed M. Mohamed, M. Hassaballah
    Pages 337-358

  17. Thresholding Algorithm Applied to Chest X-Ray Images with Pneumonia

    • Jesus Murillo-Olmos, Erick Rodríguez-Esparza, Marco Pérez-Cisneros, Daniel Zaldivar, Erik Cuevas, Gerardo Trejo-Caballero et al.
    Pages 359-407

  18. Artificial Neural Networks for Stock Market Prediction: A Comprehensive Review

    • Essam H. Houssein, Mahmoud Dirar, Kashif Hussain, Waleed M. Mohamed
    Pages 409-444

  19. Image Classification with Convolutional Neural Networks

    • Alfonso Ramos-Michel, Marco Pérez-Cisneros, Erik Cuevas, Daniel Zaldivar
    Pages 445-473

  20. Applied Machine Learning Techniques to Find Patterns and Trends in the Use of Bicycle Sharing Systems Influenced by Traffic Accidents and Violent Events in Guadalajara, Mexico

    • Adrian Barradas, Andrea Gomez-Alfaro, Rosa-María Cantón-Croda
    Pages 475-490
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Keywords

About this book

This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and implementations are included; 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 is useful in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the book is useful for research from the evolutionary computation, artificial intelligence, and image processing communities. 

Editors and Affiliations

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

    Diego Oliva, Salvador Hinojosa

  • Department of Computer Science, Faculty of Computers and Information, Minia University, Minia, Egypt

    Essam H. Houssein

Bibliographic Information

  • Book Title: Metaheuristics in Machine Learning: Theory and Applications

  • Editors: Diego Oliva, Essam H. Houssein, Salvador Hinojosa

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-030-70542-8

  • 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 2021

  • Hardcover ISBN: 978-3-030-70541-1Published: 14 July 2021

  • Softcover ISBN: 978-3-030-70544-2Published: 15 July 2022

  • eBook ISBN: 978-3-030-70542-8Published: 13 July 2021

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XIV, 769

  • Number of Illustrations: 77 b/w illustrations, 226 illustrations in colour

  • Topics: Computational Intelligence, Machine Learning

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