Metaheuristic schemes and Machine learning techniques: A synergistic perspective. (Applied Mathematical Modelling Elsevier IF=2.841, Q1)
META-MACHINE-SYNERGISTIC 2020
- URL: https://www.journals.elsevier.com/applied-mathematical-modelling/call-for-papers/metaheuristic-schemes-and-machine-learning
- Call For Paper Type: SI
- H2 Index: 0
- Submission Date: 2020-04-01
- Notification Date: 2020-10-15
- Final Version Date: 2020-12-15
Engineering & Computer Science (General)
Metaheuristic schemes and Machine learning techniques: A synergistic perspective.
Special Issue of Applied Mathematical Modelling (Elsevier IF=2.841, Q1)
We offer fast review and publication rates. All accepted articles are published free of charge.
****For submissions the system will be open from 01/01/2020********
This special issue aims to provide a collection of high-quality research articles that address broad challenges in both theoretical and application aspects of the synergistic use of metaheuristics and machine learning. We invite colleagues to contribute original research articles as well as review articles that will stimulate the continuing efforts on the combination of metaheuristic schemes and machine learning techniques. In the special issue, the contributions are mainly divided into two groups: works where machine learning is employed to enhance metaheuristics, and those in which metaheuristics are used to improve the performance of machine learning techniques.
Potential topics include, but are not limited to:
In the case of Machine learning techniques to enhance metaheuristics. Machine learning techniques such as Gaussian models, Bayesian inference, kernels, data association, Clustering, etc., for tuning metaheuristic approaches, as search mechanisms, for modifying the search structure, for selecting a certain metaheuristic for a particular problem, etc. The approaches are applied to single objective metaheuristic methods, multi-objective approaches, memetic techniques or hyper-heuristics.
In the case of metaheuristics schemes to improve the performance of machine learning techniques. They include metaheuristic methods for classification, regression, clustering, rule mining, data association, etc.
Contributions for this Special Issue are collected through an open call. All submissions will be reviewed by 3 different reviewers, according to the journal peer-review policy. Submitted papers must be unpublished and not submitted anywhere else for publication. Please submit your contribution via the online submission systems at https://ees.elsevier.com/apm/default.asp?pg=login.asp.
To ensure that all manuscripts are correctly identified for inclusion into the special issue you are editing, it is important that authors select VSI:META-MACHINE-SYNERGISTIC when they reach the “Article Type” step in the submission process. In case, if the author submits it to the incorrect article type, please bring it to our attention immediately to fix this within 2 working days.
More information in:
https://www.journals.elsevier.com/applied-mathematical-modelling/call-for-papers/metaheuristic-schemes-and-machine-learning
Important dates
Manuscript Due, 1 April 2020
First Round of Reviews, 15 October 2020
Publication Date, February 2021
Guest Editor(s)
Dr. Daniel Zaldivar
daniel.zaldivar@cucei.udg.mx
Universidad de Guadalajara
Dr. Marco Pérez
marco.perez@cucei.udg.mx
Universidad de Guadalajara
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