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A genetic/evolutionary algorithm library for Dart. Given a population of phenotypes, an evaluator (fitness function), and time, the algorithm will evolve the population until it crosses given fitness [...]

5
likes
120/ 130
pub points
58%
popularity

We analyzed this package 39 hours ago, and awarded it 120 pub points (of a possible 130):

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Follow Dart file conventions
10 / 20icon to trigger folding of the section

0/10 points: Provide a valid pubspec.yaml

The package description is too long.

Search engines display only the first part of the description. Try to keep the value of the description field in your package's pubspec.yaml file between 60 and 180 characters.

5/5 points: Provide a valid README.md

5/5 points: Provide a valid CHANGELOG.md

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Provide documentation
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10/10 points: Package has an example

  • Found example at: example/example.dart

10/10 points: 20% or more of the public API has dartdoc comments

  • 41 out of 128 API elements (32.0 %) have documentation comments.
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Platform Support
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20/20 points: Supports 6 of 6 possible platforms (iOS, Android, Web, Windows, MacOS, Linux)

  • ✓ Android
  • ✓ iOS
  • ✓ Windows
  • ✓ Linux
  • ✓ MacOS
  • ✓ Web
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Pass static analysis
30 / 30icon to trigger folding of the section

30/30 points: code has no errors, warnings, lints, or formatting issues

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Support up-to-date dependencies
20 / 20icon to trigger folding of the section

10/10 points: All of the package dependencies are supported in the latest version

PackageConstraintCompatibleLatest
meta^1.1.61.7.01.7.0

To reproduce run dart pub outdated --no-dev-dependencies --up-to-date --no-dependency-overrides.

10/10 points: Package supports latest stable Dart and Flutter SDKs

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Support sound null safety
20 / 20icon to trigger folding of the section

20/20 points: Package and dependencies are fully migrated to null safety!

Analysed with Pana 0.21.5, Dart 2.15.1.

5
likes
120
pub points
58%
popularity

Publisher

shield icon for verified publishersfiliph.net

A genetic/evolutionary algorithm library for Dart. Given a population of phenotypes, an evaluator (fitness function), and time, the algorithm will evolve the population until it crosses given fitness threshold.

Repository (GitHub)
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Documentation

API reference

License

MIT (LICENSE)

Dependencies

meta

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