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Investigating the parameter space of evolutionary algorithms

Overview of attention for article published in BioData Mining, February 2018
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#14 of 319)
  • High Attention Score compared to outputs of the same age (92nd percentile)

Mentioned by

news
1 news outlet
twitter
33 X users
facebook
3 Facebook pages
wikipedia
1 Wikipedia page
googleplus
1 Google+ user
reddit
1 Redditor

Citations

dimensions_citation
63 Dimensions

Readers on

mendeley
91 Mendeley
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Title
Investigating the parameter space of evolutionary algorithms
Published in
BioData Mining, February 2018
DOI 10.1186/s13040-018-0164-x
Pubmed ID
Authors

Moshe Sipper, Weixuan Fu, Karuna Ahuja, Jason H. Moore

Abstract

Evolutionary computation (EC) has been widely applied to biological and biomedical data. The practice of EC involves the tuning of many parameters, such as population size, generation count, selection size, and crossover and mutation rates. Through anextensiveseries of experiments over multiple evolutionary algorithm implementations and 25 problems we show that parameter space tends to be rife with viable parameters, at least for the problems studied herein. We discuss the implications of this finding in practice for the researcher employing EC.

X Demographics

X Demographics

The data shown below were collected from the profiles of 33 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 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 91 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 21%
Researcher 14 15%
Student > Master 13 14%
Student > Bachelor 9 10%
Student > Doctoral Student 5 5%
Other 14 15%
Unknown 17 19%
Readers by discipline Count As %
Computer Science 33 36%
Engineering 19 21%
Neuroscience 4 4%
Biochemistry, Genetics and Molecular Biology 3 3%
Physics and Astronomy 2 2%
Other 10 11%
Unknown 20 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 32. 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 23 April 2021.
All research outputs
#1,200,583
of 24,821,035 outputs
Outputs from BioData Mining
#14
of 319 outputs
Outputs of similar age
#26,826
of 336,042 outputs
Outputs of similar age from BioData Mining
#2
of 3 outputs
Altmetric has tracked 24,821,035 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 319 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.5. This one has done particularly well, scoring higher than 95% of its peers.
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 336,042 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.