Automated design of search algorithms: Learning on algorithmic components

This paper proposes AutoGCOP, a new general framework for automated design of local search algorithms. In a recently established General Combinatorial Optimisation Problem (GCOP) model, the problem of algorithm design itself is defined as a combinatorial optimisation problem. AutoGCOP defines a gene...

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Bibliographic Details
Published in:Expert systems with applications Vol. 185; p. 115493
Main Authors: Meng, Weiyao, Qu, Rong
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 15.12.2021
Elsevier BV
Subjects:
Algorithms
Automated algorithm design
Automation
Combinatorial analysis
Composition
Machine learning
Markov analysis
Markov chain
Markov chains
Optimization
Reinforcement learning
Route planning
Search algorithms
Vehicle routing
Windows (intervals)
Markov chain
Automated algorithm design
Search algorithms
Reinforcement learning
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:This paper proposes AutoGCOP, a new general framework for automated design of local search algorithms. In a recently established General Combinatorial Optimisation Problem (GCOP) model, the problem of algorithm design itself is defined as a combinatorial optimisation problem. AutoGCOP defines a general framework to optimise the composition of elementary algorithmic components as decision variables in GCOP. By modelling various well-known local search meta-heuristics within a general framework, AutoGCOP supports automatic design of new novel algorithms which may be highly different from those manually designed in the literature. Within the consistent AutoGCOP framework, various elementary algorithmic components are analysed for solving the benchmark vehicle routing problem with time window constraints and different characteristics. Furthermore, two learning models based on reinforcement learning and Markov chain are investigated to learn and enhance the compositions of algorithmic components towards the automated design of search algorithms. The Markov chain model presents superior performance learning the compositions of algorithmic components during the search, demonstrating its effectiveness designing new algorithms automatically. •A framework for automated design of local search algorithms.•Various local search algorithms are modelled within the framework.•The vehicle routing problems are used as the domain problem.•Performance of elementary algorithmic components is analysed.•Two learning models based on reinforcement learning and Markov chain are compared.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2021.115493