I have a feature set from which I want to select various combinations and permutations of the features. The length of a solution feature vector can range between , say 5 - 20 features , and the ordering of the features are important , meaning that feature vector ABC is different from BCA i.e they are sequential and depends on each others output.
The goal is to find many near optimal solutions around optimal solutions and the solution space is probably multi-modal, optimal solutions can be very different from each other.
From my understanding, a population based metaheuristic is needed for such task. Given that I require variable length and sequential, something like GA may work, however is there another more suitable optimization algorithm for this formulation?
