Multiple Optimisation using real data

[armenvod]

All the examples in the docs are for problems where the problem.types[:] is a range of Real numbers. Let's say I wanted to use real data for a MOEA, a good example is the fantasy football problem which I have linked below.

As you can see in the image it has currently one objective: maximising points. Let's say I want to improve it into multiobjective: maximising points & minimising cost (i.e. cost is now new objective instead of a constraint). Can I search for solutions to this problem using Platypus?

One Type I guessed may work is Subset Type, but not sure how to structure data to retain row integrity (i.e. make sure Player X is associated with their position, team, points, salary etc.)

Source(s): https://github.com/mattbrondum/Fantasy-Football-Optimization,
https://www.kdnuggets.com/2018/08/optimization-101-data-scientists.html

[dhadka]

Sure, it should work. The decision variables depend on how you want to model the problem. Using the provided image, it shows the decision variables are an array of boolean values (0/1) selecting the players in the lineup. The Binary type would work well if you can have a varying number of players per team, or if the number of players on the team is fixed you could use the Subset type.