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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization

Background:

  • Understanding algorithm performance on black-box optimization problems is crucial.
  • Existing methods often rely on strong assumptions or unavailable data.
  • Exploratory landscape analysis and algorithm selection frameworks have been developed.

Purpose of the Study:

  • To propose a novel, model-based framework for characterizing black-box optimization problems.
  • To enable straightforward comparison between different problem instances.
  • To provide a method for assessing the goodness of fit and comparing models.

Main Methods:

  • Utilizing Gaussian Process regression for a model-based approach.
  • Developing a framework for comparative analysis of problem instances.
  • Implementing and validating the framework on transformed benchmark problems.

Main Results:

  • The proposed framework allows for effective characterization and comparison of black-box optimization problems.
  • Gaussian Processes facilitate efficient model comparison and goodness-of-fit assessment.
  • Validation on transformed benchmark problems demonstrates the framework's utility.

Conclusions:

  • The new framework offers a robust and flexible method for analyzing black-box optimization problems.
  • It addresses limitations of existing approaches by reducing reliance on assumptions and unavailable information.
  • This work advances the field of exploratory landscape analysis and algorithm selection.