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Automated Algorithm Selection on Continuous Black-Box Problems by Combining Exploratory Landscape Analysis and

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  • 1Information Systems and Statistics, University of Münster, 48149 Münster, Germany kerschke@uni-muenster.de.

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Summary
This summary is machine-generated.

This study introduces an efficient algorithm selection model for continuous black-box optimization problems. The model significantly reduces computational resources needed by selecting the best-suited algorithm based on problem landscape features.

Keywords:
Automated algorithm selectionblack-box optimizationexploratory landscape analysismachine learningsingle-objective continuous optimization.

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Machine learning for optimization

Background:

  • Exploratory Landscape Analysis (ELA) features characterize problem landscapes.
  • Previous work demonstrated ELA's utility in algorithm selection models.

Purpose of the Study:

  • To demonstrate the effectiveness of ELA features in automatically constructing algorithm selection models for continuous black-box optimization.
  • To improve the efficiency of solving optimization problems compared to single best solvers.

Main Methods:

  • Utilized several years of algorithm performance data from the COCO platform.
  • Constructed a representative set of high-performing, complementary solvers.
  • Developed an algorithm selection model based on ELA features and limited function evaluations.

Main Results:

  • The developed model achieved over 50% resource reduction compared to the best single solver in the portfolio.
  • Significant efficiency gains were observed compared to classical ensemble methods.
  • The model provides increased insight into problem characteristics and algorithm properties.

Conclusions:

  • ELA features enable effective automatic construction of efficient algorithm selection models for continuous black-box optimization.
  • The model can select the best optimization algorithm for unseen problems using minimal function evaluations, potentially reusing samples for evolutionary algorithms.
  • The approach assumes the Black-Box Optimization Benchmark function set is representative of practical applications.