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Using Machine Learning Methods to Assess Module Performance Contribution in Modular Optimization Frameworks.

Ana Kostovska1, Diederick Vermetten2, Peter Korošec3

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Modular algorithm frameworks reveal key performance drivers. The elitism module in Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and linear population size reduction in differential evolution (DE) significantly impact performance.

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Area of Science:

  • Computational Science
  • Optimization Algorithms
  • Machine Learning

Background:

  • Modular algorithm frameworks offer structured analysis of algorithmic components.
  • Assessing the impact of individual modules on overall performance is crucial for algorithm design.

Purpose of the Study:

  • To propose and validate a methodology for analyzing the impact of modules on derivative-free black-box optimization algorithms.
  • To identify critical modules influencing performance in Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and differential evolution (DE).

Main Methods:

  • Analysis of performance data from 324 modCMA-ES and 576 modDE variants across 24 BBOB problems and 6 runtime budgets.
  • Exploratory data analysis of problem landscape features and their influence on algorithm performance.
  • Application of feature importance-based classifiers to predict modular configurations.

Main Results:

  • Significant variation in the impact of individual modules on overall algorithm performance.
  • Elitism module in CMA-ES and linear population size reduction in DE identified as having the most significant impact.
  • Landscape features' influence on performance varies with module configuration, though relevant features remain consistent.

Conclusions:

  • The study provides a methodology to dissect algorithm performance based on modular components.
  • Key modules significantly influence optimization performance, guiding future algorithm design.
  • Predictive models for modular configurations show comparable performance to true configurations, validating the approach.