IMHMLRW2021: Integrating meta-heuristics and machine learning for real-world optimization problem |
Website | http://diegoliva.com/cfp/imhmlrw/ |
Submission link | https://easychair.org/conferences/?conf=imhmlrw2021 |
In recent years, metaheuristics (MHs) have become essential tools for solving challenging optimization problems encountered in industry, engineering, biomedical, image processing, and the theoretical field. Several different metaheuristics exist, and new methods are under constant development. One of the most fundamental principles in our world is the search for an optimal state. Therefore, choose the right, and correct solution technique for an optimization problem can be crucially important in finding the right solutions for a given optimization problem (unconstrained and constrained optimization problems). There exist a diverse range of MHs for optimization. Optimization techniques have been used for many years in the formulation and solution of computational problems.
This book brings together outstanding research and recent developments in metaheuristics (MHs), machine learning (ML), and their applications in the industrial world. Among the subjects to be considered are theoretical developments in MHs; performance comparisons of MHs; suitable methods combining different types of approaches such as constraint programming and mathematical programming techniques; parallel and distributed MHs for multi-objective optimization; adaptation of discrete MHs to continuous optimization; dynamic optimization; software implementations; and real-life applications. Besides, Machine learning (ML) is a data analytics technique to use computational methods. Therefore, recently, MHs have been combined with several ML techniques to deal with different global and engineering optimization problems, also real-world applications. Finding an optimal solution or even sub-optimal solutions is not an easy task. Chapters published in this book describe original works in different topics in science and engineering, such as Metaheuristics, Machine learning, Soft Computing, Neural Networks, Multi-criteria decision-making, and energy efficiency, sustainable development, etc.
The book is focused on the theory and application of MA in machine learning, including hybridization and implementations in different fields. In this sense, topics are selected based on their importance and complexity in this field — for example, biochemistry, image processing, clustering, feature selection, energy, among others.
We invite all researchers and practitioners who are developing algorithms, systems, and applications, to share their results, ideas, and experiences.
Submission Guidelines
Chapter Submission
Submitted manuscripts should conform to the standard guidelines of the Springer's book chapter format. Manuscripts must be prepared using Latex or Word according to the Springer's template that can be downloaded from the (link). Manuscripts that do not follow the formatting rules will be ignored. Prospective authors should send their manuscripts electronically by easychair system in PDF. Submitted manuscripts will be refereed by at least two independent and expert reviewers for quality, correctness, originality, and relevance. The accepted contributions will be published in Intelligent Systems Reference Library by Springer. More information about Intelligent Systems Reference Library can be found (here).
Publication Schedule
The tentative schedule of the book publication is as follows:
Deadline for paper submission: A̵u̵g̵u̵s̵t̵ ̵3̵1̵,̵ ̵2̵0̵2̵1̵ November 30, 2021
First-round notification: S̵e̵p̵t̵e̵m̵b̵e̵r̵ ̵2̵0̵2̵1̵ December 2021
Camera-ready submission: O̵c̵t̵o̵b̵e̵r̵ ̵2̵0̵2̵1̵ January 2022
Publication date: 1st quarter of 2022
List of Topics
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Volume Editors
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Dr. Essam Halim Houssein, Minia University, Egypt, essam.halim(at)mu.edu.eg
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Dr. Mohamed Abd El Aziz, Zagazig University, Egypt, abd_el_aziz_m(at)yahoo.com
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Dr. Diego Oliva Universidad de Guadalajara, CUCEI, México, diego.oliva(at)cucei.udg.mx
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Dr. Laith Abualigah, Amman Arab University, Jordan, aligah.2020(at)gmail.com
More information: http://diegoliva.com/cfp/imhmlrw/