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The General Combinatorial Optimisation Problem: Towards Automated Algorithm Design

Qu, Rong; Kendall, Graham; Pillay, Nelishia

Authors

Rong Qu

Graham Kendall

Nelishia Pillay



Abstract

This paper defines a new combinatorial optimisation problem, namely General Combinatorial Optimisation Problem (GCOP), whose decision variables are a set of parametric algorithmic components, i.e. algorithm design decisions. The solutions of GCOP, i.e. compositions of algorithmic components, thus represent different generic search algorithms. The objective of GCOP is to find the optimal algorithmic compositions for solving the given optimisation problems. Solving the GCOP is thus equivalent to automatically designing the best algorithms for optimisation problems. Despite recent advances, the evolutionary computation and optimisation research communities are yet to embrace formal standards that underpin automated algorithm design. In this position paper, we establish GCOP as a new standard to define different search algorithms within one unified model. We demonstrate the new GCOP model to standardise various search algorithms as well as selection hyper-heuristics. A taxonomy is defined to distinguish several widely used terminologies in automated algorithm design, namely automated algorithm composition, configuration and selection. We would like to encourage a new line of exciting research directions addressing several challenging research issues including algorithm generality, algorithm reusability, and automated algorithm design.

Journal Article Type Article
Publication Date 2020-05
Journal IEEE Computational Intelligence Magazine
Print ISSN 1556-603X
Electronic ISSN 1556-6048
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 15
Issue 2
Pages 14-23
APA6 Citation Qu, R., Kendall, G., & Pillay, N. (2020). The General Combinatorial Optimisation Problem: Towards Automated Algorithm Design. IEEE Computational Intelligence Magazine, 15(2), 14-23. https://doi.org/10.1109/MCI.2020.2976182
DOI https://doi.org/10.1109/MCI.2020.2976182
Publisher URL https://ieeexplore.ieee.org/abstract/document/9064746
Additional Information © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

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