Multi-Objective Optimization with NSGA-II in Python

I'm looking for an expert in multi-objective optimization to help me refine some results I've been working on. I've compiled my initial results using a random forest, decision tree, and KNN in Python. Your task will be to use the NSGA-II algorithm to optimize these outcomes, specifically focusing on improving accuracy. The ideal freelancer for this project should be able to interpret the results in a manner similar to what I'll provide you.

Key requirements:
- Proficiency in Python ( Jupyter notebook
- Extensive experience with NSGA-II
- Strong understanding of multi-objective optimization
- Ability to interpret and explain complex data in a simple way