CFP: Algorithms (MDPI), Special Issue - Automation in Algorithm Design

[Mustafa MISIR]

** Apologies for cross-posting **

Dear Colleagues,

I am contacting you as the guest editor of a Special Issue titled Automation in Algorithm Design: From Machine Learning to Optimization in Algorithms (ISSN 1999-4893), MDPI. 

For more details: https://www.mdpi.com/journal/algorithms/special_issues/auto_algor_des 

All relevant original research papers and reviews are welcome.

Your submission will be subject to peer review, and published in fully open access. The deadline for the submission is 15 December 2022. You can send your manuscript anytime until the deadline as papers will be published on an ongoing basis. 

Scope:
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Automation is a widely used strategy to eliminate or limit human assistance in manufacturing. The underlying motivation behind automation is to reduce the effort required to achieve given design, planning, and control tasks with high accuracy or success. In addition to the popularity of automation in manufacturing, it has been referred to very frequently in algorithm design. In that domain, from one aspect, the idea is to automate the process of selecting the right algorithm for a given problem (~ instance). When there is already a (parametric) algorithm, the goal is to automatically configure it. If there is no useful algorithm at all, or the idea is to have your own algorithm, the strategy is to generate one in an automated way. Machine learning and optimization communities have been investigating those strategies from different perspectives with distinct research goals.

The aim of this Special Issue is to offer a common ground for those two communities to share their views on automation in algorithm design while reporting recent developments on the following non-exhaustive list of topics:

- automated machine learning
- meta-learning
- hyper-parameter optimization / tuning
- neural architecture search
- algorithm selection
- algorithm portfolios
- algorithm configuration
- parameter tuning
- parameter control
- algorithm generation
- hyper-heuristics

Best regards,

 

Mustafa MISIR

 

Assoc. Prof. of Data and Computational Science

Lead, Machine lEarning and Operations Research (MEmORy) Lab

Division of Natural and Applied Sciences

Duke Kunshan University

Duke Avenue No. 8, Kunshan, Jiangsu, China 215316  

 

Web: http://mustafamisir.github.io  |  http://memoryrlab.github.io