How to Select the Fittest Solution for a multi variable problem

I was looking for advice on how to pick solutions that are well balanced for design problems with 3 or more objectives. I see from the parallel coordinate plots there are options to select “Relative Difference Between Fitness Ranks = 0” and “Average Fitness Ranks = 0” methods. I also see there is a way to cluster the Pareto front solutions and select options from that front. The part I’m not 100% understanding is the logic behind those methods of selection. For example if I wanted to find the solution from a set that balanced its fitness for all possible objectives which of those selection methods would I use and why would I use a particular one. Or perhaps if I should be looking at all of those methods (or others) what is a good hierarchy to look at those methods and what would point me to look at one method vs the other.

I understand this is a ‘it depends’ question and apologies if its in the primer and I’ve just missed it but just looking to educate myself on what these selection tools actually mean so I can deploy them in more thoughtful ways.

Thanks ~

Hi Frederick, we recently published a paper that presents a framework for selecting solutions generated by a MOEA. It answers all your questions above and provides more information on how one can employ a methodical approach to selection that utilizes both subjective and objective methods of selection.

Reading it will be super beneficial to what you’re trying to do.

1 Like

Thank so much for sharing. Is this from the recent workshop in Europe? I really wanted to go to that (for obvious reasons) but wasn’t able to make the trek.

Anyway thanks for sharing the link and helping demystify this.

Nope, we published this after the workshop. We will be giving more workshops in the coming months (most likely at the beginning of 2023). Keep an eye out.

1 Like