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DEAP (Distributed Evolutionary Algorithms in Python) is a novel evolutionary computation framework for rapid prototyping and testing of ideas. Its design departs from most other existing frameworks in that it seeks to make algorithms explicit and data structures transparent, as opposed to the more common black box type of frameworks. It also incorporates easy parallelism where users need not concern themselves with gory implementation details like synchronization and load balancing, only functional decomposition.
The five founding hypotheses of DEAP are:
And these hypotheses lead to the following objectives:
Parts of the documentation:
Tutorial Examples |
Library Reference What's New? |
Indices and tables:
Global Module Index General Index |
Search page Complete Table of Contents |
Meta information: