Suppose we're developing a software, we have a list of requirements, their costs and a list of customers with desired requirements ordered by wishes. Each customer has a weight. Each requirement has a cost and score. Using a genetic algorithm this project is trying to find solutions using NSGA2 selection. Trying to minimise cost and maximise score.

This repo comes with a complementary report.

Using the DEAP Evolutionary Algorithms Python library. Main paper for reference: Deb et al. Reference for DEAP multi-objective-optimisation

$ pip install pipenv

$ pipenv install

$ pipenv shell

1. To see a plot comparing random, single objective and multi-objective:

$ python src/plot.py

1. To see the animation:

$ python src/animation.py

You'll need to install DEAP globally:

$ pip install deap

or using Conda

$ conda install -c conda-forge deap

Cd to project directory and:

1. To see a plot comparing random, single objective and multi-objective:

$ python src/plot.py

1. To see the animation:

$ python src/animation.py

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