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
The tentative schedule of the book publication is as follows:
Deadline for paper submission: May 30, 2020
First-round notification: June 2020
Camera-ready submission: August 2020
Publication date: 4th quarter of 2020
Dr. Diego Oliva
Departamento de Ciencias Computacionales,
Universidad de Guadalajara, CUCEI, México