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Related Experiment Videos

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

Jerry Swan1, Steven Adriænsen2, Adam D Barwell3

  • 1Dept. of Computer Science, University of York, UK jerry.swan@york.ac.uk.

Evolutionary Computation
|December 18, 2018
PubMed
Summary

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This summary is machine-generated.

Automating metaheuristic algorithm configuration is challenging due to shared state. The Automated Open-Closed Principle (AOCP) offers a solution for unintrusive reuse and automated assembly of optimization algorithms.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Metaheuristics are powerful optimization algorithms for complex problems.
  • Manual configuration of metaheuristics is time-consuming and relies on expertise.
  • Automating algorithm configuration is an emerging area of research.

Purpose of the Study:

  • To identify barriers to automating metaheuristic algorithm configuration.
  • To introduce a novel principle for facilitating automated algorithm assembly.
  • To demonstrate enhanced automation capabilities through a practical example.

Main Methods:

  • Identified 'shared state' as a key impediment to automated configuration.
  • Introduced the Automated Open-Closed Principle (AOCP).
Keywords:
Automated design of algorithmsant programmingautomatic programmingfunctional programmingmetaheuristicsprogramming by optimizationsearch-based software engineeringsystems self-assembly

Related Experiment Videos

  • Designed AOCP to support unintrusive framework reuse and component-based assembly.

    • Main Results:

    • AOCP addresses the 'shared state' problem in algorithm configuration.
    • The proposed principle enables a higher degree of automation.
    • An example implementation validated the effectiveness of AOCP.

    Conclusions:

    • The Automated Open-Closed Principle is crucial for advancing automated metaheuristic configuration.
    • AOCP facilitates the development of adaptable and easily configurable optimization tools.
    • This approach promises more efficient and accessible problem-solving for intractable problems.