We are proud to annouce the release of DEAP 0.8, a library for doing Distributed Evolutionary Algorithms in Python. You can download a copy of this release at
This release includes :
- compatibility with Python 3;
- a new algorithm : generate-update
- a lot of new examples;
- a lot of new benchmarks;
- History can now return the genealogy of a single individual;
- C++ version of the NSGA-2 algorithm
- more detailed documentation with new tutorials and examples;
- new theme for the documentation;
- and many more.
Users of DEAP 0.7 should be aware that some of the modifications included with 0.8 will break your code. Be sure to check the this page :
to find out the minor modifications that are needed to get your code fully functionnal with 0.8.
We are also proud to announce the creation of the DEAP speed project which aims at benchmarking on a daily basis the execution time of every examples provided with DEAP. Details of the project and the results are available at the following web page.
François-Michel De Rainville
Laboratoire de vision et systèmes numériques
Département de génie électrique et génie informatique
Quebec City (Quebec), Canada