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Lessons from the Evolutionary Computation Bestiary

Overview of attention for article published in Artificial Life, November 2023
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (60th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

Mentioned by

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3 X users

Citations

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4 Dimensions

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2 Mendeley
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Title
Lessons from the Evolutionary Computation Bestiary
Published in
Artificial Life, November 2023
DOI 10.1162/artl_a_00402
Pubmed ID
Authors

Felipe Campelo, Claus Aranha

Abstract

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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 1 50%
Unknown 1 50%
Readers by discipline Count As %
Computer Science 1 50%
Unknown 1 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 January 2024.
All research outputs
#14,779,306
of 25,657,205 outputs
Outputs from Artificial Life
#188
of 363 outputs
Outputs of similar age
#139,955
of 362,737 outputs
Outputs of similar age from Artificial Life
#2
of 5 outputs
Altmetric has tracked 25,657,205 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 363 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.1. This one is in the 46th percentile – i.e., 46% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 362,737 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.