Part V: Some more useful Information¶
This getting started guide has covered the most common steps of a multi-objective optimization scenario. More details about each of the topics shown in this guide are available in the corresponding topic sections.
Overview
Interface: An overview over the most important parameters of the interface.
Problems: A guide how to implement your own custom problem and how to use test problems alrady being implemented.
Algorithms: Information about all algorithms and how to use them for optimization.
Operators: An overview of evolutionary operators.
Customization: How to design your custom evolutionary operators to develop an efficient genetic algorithm for your specific optimization problem.
Visualization: Different techniques for visualization the results of an optimization run or a single solution.
Multi-Criteria Decision Making: How to select a solution from a solution set.
FAQ: Frequently asked questions.
We hope you have enjoyed the getting started guide. We refer to each section covered on the landing page. If you have any questions or concerns, do not hesitate to contact us.
Cite Us¶
If you have used our framework for research purposes, you can cite our publication by:
@ARTICLE{pymoo,
author={J. {Blank} and K. {Deb}},
journal={IEEE Access},
title={pymoo: Multi-Objective Optimization in Python},
year={2020},
volume={8},
number={},
pages={89497-89509},
}