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Lessons from the Evolutionary Computation Bestiary.

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  • 1Aston University, College of Engineering and Physical Sciences. f.campelo@aston.ac.uk.

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Metaheuristics, inspired by nature, now suffers from numerous metaphor-centered algorithms. This trend hinders scientific progress by creating redundant variants and obscuring generalizable optimization principles.

Keywords:
Metaheuristicscritical analysisdiscussion

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization

Background:

  • Metaheuristics historically drew inspiration from natural systems like evolution and swarm behavior.
  • Recent decades show a proliferation of metaphor-centered algorithms inspired by diverse and sometimes unusual phenomena.

Purpose of the Study:

  • To analyze the trend of metaphor-driven metaheuristics.
  • To discuss the negative consequences of this trend on scientific progress.
  • To propose a more balanced approach to metaheuristic development.

Main Methods:

  • Literature review and conceptual analysis of metaheuristic algorithm trends.
  • Discussion of the impact of naming conventions and algorithmic variants.
  • Exploration of potential causes and solutions for the observed trend.

Main Results:

  • The field is saturated with numerous, subtly different algorithms lacking distinct contributions.
  • This proliferation hinders understanding of biological systems and the development of generalizable optimization principles.
  • Lack of scientific rigor in some metaphor-based approaches.

Conclusions:

  • The excessive use of metaphors in metaheuristics has become counterproductive.
  • A shift towards greater scientific soundness and less reliance on superficial inspiration is needed.
  • Encouraging research that focuses on fundamental principles rather than novel metaphors will benefit the field.