Initial release 2009 (2009) | Development status Active | |
Original author(s) Developer(s) François-Michel De Rainville, Félix-Antoine Fortin, Marc-André Gardner Stable release 1.0.2 / May 13, 2015; 21 months ago (2015-05-13) | ||
Distributed Evolutionary Algorithms in Python (DEAP) is an evolutionary computation framework for rapid prototyping and testing of ideas . It incorporates the data structures and tools required to implement most common evolutionary computation techniques such as genetic algorithm, genetic programming, evolution strategies, particle swarm optimization, differential evolution and estimation of distribution algorithm. It is developed at Université Laval since 2009.
Example
The following code gives a quick overview how the Onemax problem optimization with genetic algorithm can be implemented with DEAP.
References
DEAP (software) Wikipedia(Text) CC BY-SA