Dear Researchers,
I'm looking for the implementation of advanced metaheuristic algorithms in Python. I would be incredibly thankful if someone could assist me with the execution of evolutionary algorithms or can provide me with the necessary codes in Python.
Thank you very much.
Dear Muhammad Kamran,
I suggest you see links on the topic.
https://www.scitepress.org/papers/2016/60482/pdf/index.html
https://medium.com/analytics-vidhya/optimization-modelling-in-python-metaheuristics-with-constraints-c22b08c487e8
https://staff.fmi.uvt.ro/~daniela.zaharie/ma2019/Projects/ProjectsMetaheuristics2019.pdf
https://machinelearningmastery.com/simple-genetic-algorithm-from-scratch-in-python/
https://towardsdatascience.com/tagged/metaheuristics
https://www.upwork.com/services/product/a-code-for-metaheuristic-and-evolutionary-algorithm-in-matlab-or-python-1420759174585835520
Article VecMetaPy: A vectorized framework for metaheuristic optimiza...
Best regards
Thank you very much respected Dr. Mohamed-Mourad Lafifi . The links are extremely instructive.
DEAP (https://github.com/DEAP/deap) is a popular library for evolutionary algorithms in Python. It's been around a long time and is still being maintained.
Hi,
Try watching this video of the Python implementation of the GA algorithm:
https://www.youtube.com/watch?v=PhJgktRB1AM
From there, you can find more sample python codes on github
Have fun learning!
Dear Muhammad,
In the website of Professor Mirjalili who is one of the pivotal pioneers in Swarm Intelligence, you can find several modern meta-heuristics' source codes which have been developed in different languages like Python. Matlab, R, and C++ and so on. Please find the codes in the below website:
https://seyedalimirjalili.com/projects
Good luck
Dear Muhammad,
In addition to DEAP which is a great one, I suggest another library : PyMOO which also provides complementary single & multi objectives solvers for discrete and continuous variables problems.
https://pymoo.org/algorithms/index.html
Best regards !
You can check the PyGAD library: https://pygad.readthedocs.io
It is a very easy-to-use Python library to build the genetic algorithm.
Opytimizer is also an intuitive library, designed to be straightforward, well-documented and easily plug-in-played into diverse applications: https://github.com/gugarosa/opytimizer
It is not necessarily focused on evolutionary algorithms, but mostly on well-known meta-heuristic techniques.
Another one that is mostly focused on nature-inspired algorithms (not only evolutionary ones) is NiaPy: https://github.com/NiaOrg/NiaPy
with its documentation: https://niapy.org/en/stable/
There are also some others, like jMetalPy (https://github.com/jMetal/jMetalPy) and PySwarms (https://github.com/ljvmiranda921/pyswarms).
For Evolutionary Algorithms, though, I think that DEAP (which is mentioned by Prof. Michael Lones above) is a good choice.
you can use this link
https://github.com/MorvanZhou/Evolutionary-Algorithm
I have been using both DEAP and PYMOO and highly recommend PYMOO. Also, there is also Platypus which seems to have MPI parallelization as well.
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