DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. It seeks to make algorithms explicit and data structures transparent. It works in perfect harmony with parallelisation mechanism such as multiprocessing and SCOOP. The following documentation presents the key concepts and many features to build your own evolutions.
Warning
If your are inheriting from numpy.ndarray see the tutorials/advanced/numpy tutorial and the /examples/ga_onemax_numpy example.
Getting Help
Having trouble? We’d like to help!
- Search for information in the archives of the deap-users mailing list, or post a question.
- Report bugs with DEAP in our issue tracker.
- First steps:
Overview (Start Here!) <overview>Installation <installation>Porting Guide <porting>
- Basic tutorials:
Part 1: creating types <tutorials/basic/part1>Part 2: operators and algorithms <tutorials/basic/part2>Part 3: logging statistics <tutorials/basic/part3>Part 4: using multiple processors <tutorials/basic/part4>
- Advanced tutorials:
tutorials/advanced/gptutorials/advanced/checkpointtutorials/advanced/benchmarkingtutorials/advanced/numpy
examples/indexapi/indexreleasescontributingabout
overview installation porting tutorials/basic/part1 tutorials/basic/part2 tutorials/basic/part3 tutorials/basic/part4 tutorials/advanced/gp tutorials/advanced/checkpoint tutorials/advanced/benchmarking tutorials/advanced/numpy examples/index api/index releases contributing about