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Generating New Space-Filling Test Instances for Continuous Black-Box Optimization.

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Summary
This summary is machine-generated.

This study introduces a novel method for creating diverse and challenging test instances for continuous black-box optimization problems. The technique uses genetic programming to generate new functions that effectively test algorithm performance.

Keywords:
Algorithm selectionbenchmarkingblack-box continuous optimizationexploratory landscape analysisinstance generator.

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Area of Science:

  • Optimization
  • Computational Science
  • Algorithm Development

Background:

  • Continuous black-box optimization requires diverse and challenging test instances to accurately assess algorithm performance.
  • Existing benchmark sets may not adequately cover the full spectrum of problem complexities.
  • Developing new test instances with controllable characteristics is crucial for advancing optimization algorithms.

Purpose of the Study:

  • To present a novel method for generating diverse and challenging test instances for continuous black-box optimization.
  • To enable the creation of test instances with controllable characteristics.
  • To improve the evaluation and development of optimization algorithms.

Main Methods:

  • Representing test instances as feature vectors of exploratory landscape analysis measures.
  • Projecting features into a 2D instance space for visualization and analysis.
  • Employing genetic programming to evolve functions with controllable characteristics.
  • Using convergence to target points in the instance space to guide the generation process.

Main Results:

  • Successfully generated diverse and challenging 2D and 10D test functions.
  • Demonstrated the ability to recreate existing test functions and generate novel ones.
  • The new set of instances proved more diverse and challenging for state-of-the-art algorithms compared to a benchmark set.
  • Validated the method's scalability and effectiveness.

Conclusions:

  • The proposed method offers a new approach for developing test instances with controllable characteristics for continuous black-box optimization.
  • This advancement is essential for exposing algorithm strengths and weaknesses, thereby driving further algorithm development.
  • The generated instances provide a more comprehensive and rigorous benchmark for evaluating optimization algorithms.