# pymhopt: Python Wrapper for Optimization Algorithms
This Python 3 code provides wrapper for symbolic regression providing two implementations a) Genetic Programming with symbolic regression b) multi-objective genetic programming using NSGA-II (https://ieeexplore.ieee.org/document/996017).
## Dependencies pandas; numpy; scikit-learn; gplearn; graphviz.
## Installation Run pip install pymhopt
## Example See test.py for an example.
## Acknowledgements & Credits
The gplearn module is used as base for symbolic expression and genetic programming evaluations. (https://gplearn.readthedocs.io/en/stable/)
The nsga-II algorithm is adapted from [marcovirgolin repo](https://github.com/marcovirgolin/pyNSGP/).
A fast and elitist multiobjective genetic algorithm: NSGA-II (https://ieeexplore.ieee.org/document/996017)
Note:- This package is still under development & WORK IN PROGRESS, I will aim to cover a bunch of different meta-heuristics optimization algorithms.
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