https://github.com/jpodivin/pystrand

Genetic algorithm library
https://github.com/jpodivin/pystrand

artificial-intelligence genetic-algorithm library python

Last synced: 1 day ago
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Genetic algorithm library

• Host: GitHub
• URL: https://github.com/jpodivin/pystrand
• Owner: jpodivin
• License: bsd-3-clause
• Created: 2020-08-06T07:23:01.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2024-09-21T13:03:06.000Z (about 2 months ago)
• Last Synced: 2024-10-06T23:04:31.178Z (about 1 month ago)
• Topics: artificial-intelligence, genetic-algorithm, library, python
• Language: Python
• Homepage:
• Size: 223 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# pystrand

![build](https://github.com/jpodivin/pystrand/actions/workflows/build.yml/badge.svg)

## Quickstart

In order to define a genetic algorithm you only need to determine

how should genotypes, or candidate solutions, look and which operators

should apply to them.

The rest is simply matter of providing training data.

In our case the data was generated by function `f(x) = 5 + 5x + 2x^2`.

```

   >>>x = [i for i in range(10)]

   >>>y = [5 + (5*i) +(2*(i**2)) for i in x]

```

After importing the model class we define which values can genes take.

```

   >>>from pystrand.models.polymodels import PowerPolyModel

   >>>domain = [i/10 for i in range(-100, 100)]

```

At the model initialization we can provide constraints on operator behavior, population size

and maximum allowed runtime. This is particulary useful if we are looking for approximate solution.

```

 >>> model = PowerPolyModel(domain, population_size=500, max_iterations=1000, crossover_prob=0.5)

```

We can also initialize the model with no constraints on the behavior of the algorithm, which will

set parameters to pre-determined defaults.

The only required parameter is the domain of gene values.

```

 >>> model = PowerPolyModel(domain)

```

Model is then fitted on provided data. 

```

   >>>model.fit(x, y, verbose=0)

   >>>model.solution

   ... (1., Genotype([5., 5., 2., 0., 0., 0., 0., 0., 0., 0.]))

```