A evolutionary algorithm library for Dart. Given a population of agents, an evaluator (fitness function), and time, the algorithm will evolve the population until it crosses given fitness threshold.
Evolution Strategies, sometimes also referred to as Evolutionary Strategies, and Evolutionary Programming are search paradigms inspired by the principles of biological evolution. They belong to the family of evolutionary algorithms that address optimization problems by implementing a repeated process of stochastic variations followed by selection: in each iteration (or generation), new candidate solutions (or offspring) are generated from previous candidate solutions (parents), their fitness is evaluated, and the better candidate solutions are selected to become the generators for the next iteration.
Read more about differential evolution on Wikipedia.
Add this to your package's pubspec.yaml file:
dependencies: evo: ^1.0.1+1
You can install packages from the command line:
$ pub get
Alternatively, your editor might support
Check the docs for your editor to learn more.
Now in your Dart code, you can use:
|1.0.1+1||Dec 8, 2017|
|1.0.1||Nov 25, 2017|
|1.0.0||Nov 25, 2017|
|0.0.3||Nov 25, 2017|
|0.0.2||Nov 25, 2017|
|0.0.1||Nov 25, 2017|
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The package version is not analyzed, because it does not support Dart 2. Until this is resolved, the package will receive a health and maintenance score of 0.
Support Dart 2 in
The SDK constraint in
pubspec.yaml doesn't allow the Dart 2.0.0 release. For information about upgrading it to be Dart 2 compatible, please see https://www.dartlang.org/dart-2#migration.
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|Dart SDK||>=1.8.0 <2.0.0|