Extending the "Open-Closed Principle" to Automated Algorithm Configuration

Metaheuristics are an effective and diverse class of optimization algorithms: a means of obtaining solutions of acceptable quality for otherwise intractable problems. The selection, construction, and configuration of a metaheuristic for a given problem has historically been a manually intensive proc...

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Veröffentlicht in:Evolutionary computation Jg. 27; H. 1; S. 173
Hauptverfasser: Swan, Jerry, Adriænsen, Steven, Barwell, Adam D, Hammond, Kevin, White, David R
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 01.03.2019
Schlagworte:
automatic programming
Automated design of algorithms
systems self-assembly
programming by optimization
functional programming
search-based software engineering
ant programming
metaheuristics
ISSN:1530-9304, 1530-9304
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Zusammenfassung:Metaheuristics are an effective and diverse class of optimization algorithms: a means of obtaining solutions of acceptable quality for otherwise intractable problems. The selection, construction, and configuration of a metaheuristic for a given problem has historically been a manually intensive process based on experience, experimentation, and reasoning by metaphor. More recently, there has been interest in automating the process of algorithm configuration. In this article, we identify shared state as an inhibitor of progress for such automation. To solve this problem, we introduce the Automated Open-Closed Principle (AOCP), which stipulates design requirements for unintrusive reuse of algorithm frameworks and automated assembly of algorithms from an extensible palette of components. We demonstrate how the AOCP enables a greater degree of automation than previously possible via an example implementation.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:1530-9304
1530-9304
DOI:10.1162/evco_a_00245