Extending the ‘Open-Closed Principle’ to automated algorithm configuration

Jerry Swan, Stephen Adriænsen, Adam David Barwell, Kevin Hammond, David White

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
19 Downloads (Pure)

Abstract

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 paper, 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.
Original languageEnglish
Pages (from-to)173-193
Number of pages21
JournalEvolutionary Computation
Volume27
Issue number1
Early online date17 Dec 2018
DOIs
Publication statusPublished - 4 Mar 2019

Keywords

  • Automated design of algorithms
  • Automatic programming
  • Programming by optimization
  • Metaheuristics
  • Functional programming
  • Ant programming
  • Search based software engineering
  • Systems self assembly

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