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Automatically Designing State-of-the-Art Multi- and Many-Objective Evolutionary Algorithms.

Leonardo C T Bezerra1, Manuel López-Ibáñez2, Thomas Stützle3

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Summary
This summary is machine-generated.

This study automatically designs high-performing multiobjective evolutionary algorithms (MOEAs) for continuous optimization. The automated approach uses a configurable framework and multiple performance metrics to achieve state-of-the-art results.

Keywords:
Multiobjective optimizationautomatic algorithm design.evolutionary algorithms

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Evolutionary computation

Background:

  • Existing multiobjective evolutionary algorithms (MOEAs) require extensive parameter tuning.
  • Identifying state-of-the-art MOEAs necessitates considering automatic configuration, diverse setups, and multiple performance metrics.

Purpose of the Study:

  • To automatically devise MOEAs with verified state-of-the-art performance for multi- and many-objective continuous optimization.
  • To extend a configurable algorithmic framework for automated MOEA design.
  • To propose a multiobjective formulation for automatic MOEA design, addressing metric disagreement in many-objective optimization.

Main Methods:

  • Extending a configurable algorithmic framework with more MOEAs, evolutionary algorithms, and search paradigms.
  • Employing an automatic configuration method to instantiate high-performing MOEA designs.
  • Developing a multiobjective formulation for automatic MOEA design to optimize multiple performance metrics.

Main Results:

  • Automatically designed MOEAs demonstrate state-of-the-art performance in continuous optimization.
  • The extended framework facilitates the automated creation of effective MOEA designs.
  • The multiobjective formulation for design ensures robust performance across various metrics, especially in many-objective scenarios.

Conclusions:

  • Automated design of MOEAs is a viable approach to achieve state-of-the-art performance.
  • A configurable framework and multiobjective optimization for design are crucial for developing advanced MOEAs.
  • The proposed methods offer a systematic way to discover high-performing MOEAs for complex optimization problems.