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MO-SMAC: Multiobjective Sequential Model-Based Algorithm Configuration.

Jeroen G Rook1, Carolin Benjamins2, Jakob Bossek3

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Summary
This summary is machine-generated.

This study introduces a new multi-objective automated algorithm configurator to find optimal AI parameters for complex tasks. It enhances trustworthy and resource-efficient AI by approximating the Pareto set for multiple objectives.

Keywords:
Automated algorithm configurationBayesian optimizationmultiobjective optimization

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Optimization

Background:

  • Automated algorithm configuration (AAC) traditionally focuses on single objectives.
  • Real-world AI tasks often involve multiple, potentially conflicting, performance objectives.
  • There is a growing need for trustworthy and resource-efficient AI systems, necessitating multi-objective optimization.

Purpose of the Study:

  • To develop a general-purpose multi-objective automated algorithm configurator.
  • To extend the widely-used SMAC framework for multi-objective optimization.
  • To search for a non-dominated set approximating the true Pareto set.

Main Methods:

  • Proposed a pure multi-objective Bayesian Optimization approach.
  • Utilized predicted hypervolume improvement as the acquisition function.
  • Introduced a novel intensification procedure for efficient multi-objective configuration selection.

Main Results:

  • Empirically validated the approach across four AI domains.
  • Demonstrated superior performance compared to baseline methods.
  • Achieved competitiveness with MO-ParamILS on specific scenarios, showing overall best performance.

Conclusions:

  • The proposed multi-objective configurator effectively handles complex AI optimization tasks.
  • The method advances the field of automated algorithm configuration towards more realistic multi-objective scenarios.
  • This work contributes to the development of more reliable and efficient AI systems.