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Performance Analysis of Continuous Black-Box Optimization Algorithms via Footprints in Instance Space.

Mario A Muñoz1, Kate A Smith-Miles2

  • 1School of Mathematical Sciences, Monash University, Clayton, Victoria 3800 Australia mario.munoz@monash.edu.

Evolutionary Computation
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PubMed
Summary
This summary is machine-generated.

This study introduces a new method to objectively assess algorithm performance by analyzing problem instances and algorithm behavior. It helps identify algorithm weaknesses and potential phase transitions in performance for better algorithm selection.

Keywords:
Algorithm selectionblack-box continuous optimizationexploratory landscape analysisfootprint analysisperformance prediction

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Algorithm Analysis

Background:

  • Traditional algorithm assessment often relies on benchmark performance or targeted problem generation.
  • Understanding algorithm limitations and failure points is crucial for robust system design.
  • Identifying phase transitions in algorithm performance remains a challenge.

Purpose of the Study:

  • To present a novel method for the objective assessment of algorithm strengths and weaknesses.
  • To quantify both the nature of test instances and algorithm performance.
  • To identify potential phase transitions in algorithm performance.

Main Methods:

  • Estimating and characterizing algorithm footprints (regions of instance space for expected good performance).
  • Selecting features to generate a common instance space and validating it with a prediction model.
  • Characterizing footprints by area and density.

Main Results:

  • The method quantifies instance characteristics and algorithm performance.
  • Algorithm footprints are estimated for individual algorithms and portfolios.
  • Complementary performance between algorithms is identified.

Conclusions:

  • The developed method objectively assesses algorithms by analyzing instance space and performance.
  • It quantifies features of hard problems and locates potential phase transition regions.
  • This approach aids in understanding algorithm behavior and improving algorithm selection.