NiaPy’s documentation¶
Python micro framework for building nature-inspired algorithms.
Nature-inspired algorithms are a very popular tool for solving optimization problems. Since the beginning of their era, numerous variants of nature-inspired algorithms were developed. To prove their versatility, those were tested in various domains on various applications, especially when they are hybridized, modified or adapted. However, implementation of nature-inspired algorithms is sometimes difficult, complex and tedious task. In order to break this wall, NiaPy is intended for simple and quick use, without spending a time for implementing algorithms from scratch.
The main documentation is organized into a couple sections:
- Changelog
- 2.0.0rc10 (2019-11-12)
- 2.0.0rc9 (2019-11-11)
- 2.0.0rc8 (2019-11-11)
- 2.0.0rc7 (2019-11-11)
- 2.0.0rc6 (2019-11-11)
- 2.0.0rc5 (2019-05-06)
- 2.0.0rc4 (2018-11-30)
- 2.0.0rc3 (2018-11-30)
- 1.0.2 (2018-10-24)
- 2 (2018-08-30)
- 2.0.0rc2 (2018-08-30)
- 2.0.0rc1 (2018-08-30)
- 1.0.1 (2018-03-21)
- 1.0.0 (2018-02-28)
- 1.0.0rc2 (2018-02-28)
- 1.0.0rc1 (2018-02-28)
- 0.1.3a2 (2018-02-26)
- 0.1.3a1 (2018-02-26)
- 0.1.2a4 (2018-02-26)
- 0.1.2a3 (2018-02-26)
- 0.1.2a2 (2018-02-26)
- 0.1.2a1 (2018-02-26)
- Installation
- Testing
- Documentation
- API