I am scanning the internet for libraries available to use GA with potential development for multi-objective algorithms like NSGAII for Python. Do you have any suggestion?
Here is what I have so far:
The question is not necessarily about which one is better but more about the features of these libraries and the possibility to switch easily from single to multi-objective optimization.
Thank you
Multi-objectives Genetic Algorithm (MOGA) is one of many engineering optimization techniques, a guided random search method. It is suitable for solving multi-objective optimization related problems with the capability to explore the diverse regions of the solution space.
Abstract: Python has become the programming language of choice for research and industry projects related to data science, machine learning, and deep learning.
However, multiobjective evolutionary algorithms (MOGA), seem to be the best method used nowadays. One of their main advantages is that they are population based, thus finding more than one interesting solution in a single run. Another advantage is the lack of assumptions about the problem to be solved.
Disclosure: I am of one of the developers of DEAP.
DEAP is the most actively developed project amongst the ones mentioned. It has an active mailing-list, which is an interesting feature if you need help at some point. The class creation which is unique to DEAP makes switching from single to multiple objectives really easy. It comes with multiple examples, including examples of multiobjective genetic algorithms.
It is also compatible with both Python 2 and 3, while some other frameworks only support Python 2. Finally, while it is written in pure Python, we will always have performances in mind, so it is quite fast. Timing of the different examples are available at http://deap.gel.ulaval.ca/speed/.
Pybrain seems to have GA and multiobjective GA:
http://pybrain.org/docs/api/optimization/optimization.html?highlight=genetic#population-based
Still seems to be a bit basic. I didn't try it so I can't tell you how good it is.
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