I am trying to design a greedy algorithm that has to take in multiple factors when making a greedy choice.
Any item has an item weight of
Iw and item size of
Is, these are both numerical values. Item sizes are originally translated from the values very small, small, medium, large, extra large to .1, .5, 1, 2, 2.5, respectively.
Imagine you are trying to optimize a box to have as many items in it as possible while being under a weight limit
Tw and a size limit
Ts. Also, the distribution of the size types (small, medium, etc) has to be a sort of bell curve where most of the items are medium, a smaller portion are small/large, and the least are extra large/very small.
I am familiar with creating a GA with only one variable, but am not sure how to do it with all of these factors.