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General subpopulation framework and taming the conflict inside populations.

Danilo Vasconcellos Vargas1, Junichi Murata, Hirotaka Takano

  • 1Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka, 819-0395, Japan vargas@cig.ees.kyushu-u.ac.jp.

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Summary

Structured evolutionary algorithms offer benefits in multi-objective optimization. A new general subpopulation framework improves algorithm design and performance, outperforming traditional methods by reducing detrimental strategy competition.

Keywords:
Structured evolutionary algorithmsgeneral differential evolutiongeneral subpopulation frameworkhybridizationmulti-objective algorithmsnovelty searchparallel evolutionary algorithms

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Evolutionary computation

Background:

  • Structured evolutionary algorithms (SEAs) are under-explored in multi-objective optimization.
  • Complex dynamics and structures limit the understanding and adoption of SEAs.
  • Existing SEAs include cellular algorithms, island models, and restricted mating algorithms.

Purpose of the Study:

  • To propose a general subpopulation framework for designing structured evolutionary algorithms.
  • To integrate various optimization algorithms without restrictions.
  • To enhance the performance of multi-objective optimization.

Main Methods:

  • Developed a general subpopulation framework adaptable to various structured evolutionary algorithms.
  • Proposed two novel algorithms based on the general subpopulation framework.
  • Integrated single-objective differential evolution algorithms within the subpopulation framework.

Main Results:

  • The proposed framework generalizes existing structured evolutionary algorithms.
  • Algorithms based on the framework significantly improved results, even with poorly performing individual components.
  • Subpopulation algorithms outperformed their panmictic counterparts.

Conclusions:

  • The general subpopulation framework facilitates the design and integration of structured evolutionary algorithms.
  • Competition between strategies within a single population can be detrimental.
  • Employing a subpopulation framework offers significant benefits for multi-objective optimization.