Design of large-scale metaheuristic component studies

Stegherr, Helena; Heider, Michael; Luley, Leopold; Hähner, Jörg

doi:10.1145/3449726.3463168

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…A recent comprehensive taxonomy of natureand bio-inspired optimisation approaches suggests as many as 360 unique metaphors in the meta-heuristics literature (Molina et al, 2020). This massive amount of distinct algorithms, each claiming to present a unique way to solve optimisation problems (in most cases limited to continuous and unconstrained formulations) is at odds with the relatively simplistic structure of most of these techniques (once the unnecessary metaphor-heavy language is stripped), as well as with the existence of general algorithmic design patterns that generalize many of these techniques (de Armas et al, 2021;de Jong, 2006;Stegherr & Hähn, 2021;Stegherr et al, 2020). This explosion of metaphor-centred methods has led to an intense fragmentation of the literature into tens, perhaps hundreds, of small, barely-discernible niches.…”

Section: Why This Is a Problemmentioning

confidence: 99%

“…Several authors have recently proposed taxonomies of search-based optimisation methods, where several algorithms are explained by an unifying framework and its associated components Molina et al, 2020;Stegherr and Hähn, 2021;Stegherr et al, 2020;Stork et al, 2020. Some of these works go so far as describing specific code for the framework and its components, and using this code to re-implement some of the existing metaphor methods (Cruz-Duarte et al, 2020;de Armas et al, 2021).…”

Section: Potential Solutionsmentioning

confidence: 99%

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Lessons from the Evolutionary Computation Bestiary

Campelo

Aranha

2023

Artificial Life

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The field of metaheuristics has a long history of finding inspiration in natural systems, starting from evolution strategies, genetic algorithms, and ant colony optimization in the second half of the 20th century. In the last decades, however, the field has experienced an explosion of metaphor-centered methods claiming to be inspired by increasingly absurd natural (and even supernatural) phenomena—several different types of birds, mammals, fish and invertebrates, soccer and volleyball, reincarnation, zombies, and gods. Although metaphors can be powerful inspiration tools, the emergence of hundreds of barely discernible algorithmic variants under different labels and nomenclatures has been counterproductive to the scientific progress of the field, as it neither improves our ability to understand and simulate biological systems nor contributes generalizable knowledge or design principles for global optimization approaches. In this article we discuss some of the possible causes of this trend, its negative consequences for the field, and some efforts aimed at moving the area of metaheuristics toward a better balance between inspiration and scientific soundness.

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