Automation in Algorithm Design: From Machine Learning to Optimization
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 931
Special Issue Editor
Special Issue Information
Dear Colleagues,
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 in this field.
Dr. Mustafa Mısır
Guest Editor
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Keywords
- automated machine learning
- meta-learning
- hyper-parameter optimiztion/tuning
- neural architecture search
- algorithm selection
- algorithm configuration
- parameter tuning
- parameter control
- algorithm generation
- hyper-heuristics
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