Call for Papers: Special Issue on “Optimization and Machine Learning”

Guest Editors

El-Ghazali Talbi
University of Lille, France

Celso Ribeiro
Universidade Federal Fluminense, Brazil

This special issue will provide an opportunity to the international research community in optimization and machine learning to publish recent research results in the application of machine learning approaches to optimization methodologies (e.g. metaheuristics, mathematical programming, constraint programming, exact algorithms, heuristics).

This special issue welcomes papers that cover any aspects of the application of machine learning to optimization such as parameter tuning, hybridization, optimization-simulation, meta-modeling, algorithm configuration, surrogate modeling, real-life applications, high-performance computing, multi-objective optimization.

The deadline for submissions is May 31, 2018.

Although we strongly encourage submissions from authors that will present their work in the OLA’2018 International Workshop on Optimization and Learning, which will be held in Alicante, Spain, from February 26 to 28, 2018, this Call for Papers is also open to and welcomes submissions from the entire community of academics and practitioners.

Each paper will be peer-reviewed according to the editorial policy of the International Transactions in Operational Research (ITOR), published by the International Federation of Operational Research Societies (IFORS). Papers should be original, unpublished, and not currently under consideration for publication elsewhere. They should be prepared according to the instructions to authors that can be found on the journal homepage. Authors should upload their contributions using the submission site http://mc.manuscriptcentral.com/itor, indicating in their cover letter that the paper is intended for this special issue. Other inquiries should be sent directly to either of the Guest Editors in charge of this issue: El-Ghazali Talbi (el-ghazali.talbi@univ-lille1.fr) and Celso Ribeiro (celso@ic.uff.br).

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