Extending the ‘Open-Closed Principle’ to automated algorithm configuration
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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.
Swan , J , Adriænsen , S , Barwell , A D , Hammond , K & White , D 2019 , ' Extending the ‘Open-Closed Principle’ to automated algorithm configuration ' , Evolutionary Computation , vol. 27 , no. 1 , pp. 173-193 . https://doi.org/10.1162/evco_a_00245
© 2018, Massachusetts Institute of Technology. This work has been made available online in accordance with the publisher's policies. This is the author created accepted version manuscript following peer review and as such may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1162/evco_a_00245
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