Call for Book Chapters
Metaheuristics in machine learning: theory and applications
To be published by Studies in Computational Intelligence by Springer http://www.springer.com/series/7092
***COVID 19 UPDATE: DEADLINE EXTENDED***
Optimization is present if many fields of science and technology because it is always necessary to find the best value in different implementations. In recent years the use of metaheuristic algorithms (MA) have been increasing due to its flexibility and easy implementation. MA are optimization tools designed to work with difficult problems where a set of solutions is created to explore the search space. Usually, MA can behave in two forms; exploration, and exploitation. The exploration phase is used to diversify the solutions along the search space to avoid local stagnation, while the exploitation phase intensifies the search in a region to ensure convergence.
On the other hand, machine learning (ML) is a field that has also attracted the attention in different fields of application. ML techniques are used to analyse and classify different amounts of information. For that reason, they are very popular. However, depending on the algorithm used they exist different drawbacks, for example during the training processes. Different issues in ML can be addressed as optimization problems and it is a tendency to use MA to help the ML approaches.
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.
Topics of interest include, but are not limited to, the following:
- Hybrid Metaheuristics
- Theoretical aspects of hybridization
- Automated parameter tuning
- Evolutionary Computation Algorithms
- Swarm Optimization
- Multi-objective optimization
- Multilevel segmentation
- Feature selection
- Reinforcement learning
- Supervised learning
- Pattern recognition
- Neural networks
- Deep learning
- Fuzzy systems
- Rough sets
- Computer vision
- Image processing
- Feature extraction
- Quantum Optimization
- Image thresholding
Submitted manuscripts should conform to the standard guidelines of Springer’s book chapter format. Manuscripts must be prepared using Latex or Word according to 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).
Link for submission
Publication Schedule ***COVID 19 UPDATE***
The tentative schedule of the book publication is as follows:
Deadline for paper submission: August 31, 2020 (EXTENDED)
First-round notification: September 2020 (EXTENDED)
Camera-ready submission: October 2020 (EXTENDED)
Publication date: 1st quarter of 2021
Selected papers from the International Conference on Business Management, Innovation and Sustainability in the Age of Possibilities (ICBMIS 2020) will be published in this book.
About the Conference
The conference will be held in our state of art Amity campus in Dubai, the most modern and fascinating city in the Middle East.
The primary objective of the conference is to bring together researchers, scholars, and industry participants from around the world to share their experiences and latest research results in all aspects of Business Management, Technology, and Business Innovation, Innovation & Entrepreneurship, Sustainability in Technology Management, and Sustainable Converging Technology.
More information: https://amityuniversity.ae/icbmis/index.php
Dr. Diego Oliva
Departamento de Ciencias Computacionales,
Universidad de Guadalajara, CUCEI, México