I've been recently working with a combinatorial optimization problem defined as follows. Given two sets of items, A and B, select the best combination of these items given a scoring function $f(A,B) \rightarrow \mathbb{R}$. The goal is to maximize the output.

As I am new to this field, my initial impression was, to try and code up a stochastic optimizer, such as for example genetic algorithm to solve this. My question is, are there any libraries apart from $\textit{Deap}$, which offer such functionality.

Thank you.

  • $\begingroup$ I face the exact problem. Did you find a solution? $\endgroup$
    – lenhhoxung
    Dec 23, 2021 at 10:12

1 Answer 1


The problem you are trying to solve is a variant of the two-way partitioning problem (pg. 234/730 in the pdf http://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf) which is known to be NP hard.

What this means in layman terms is that if the size of A and B is large, the problem can basically not be solved exactly. At best, one can hope to get algorithms which say statements of the form "We are off by a factor of at most k".

In these cases, the details become important. I would recommend you not code up such algorithms yourself, because they will likely be very inefficient (and maybe even way off!).

The library is not the issue here; the functional form of $f$ can dramatically change the approach

  • $\begingroup$ Whether it is NP hard depends on the nature of f. For example if f is nonzero for exactly one partition, then the problem is trivial. $\endgroup$
    – Zach Boyd
    May 20, 2019 at 19:14
  • $\begingroup$ So, not coding it myself would imply I can use a libraray of some form? Which one has support for this? $\endgroup$ May 29, 2019 at 5:57
  • $\begingroup$ I'm trying to solve this problem for a small input. I don't care if it's NP hard. Is there a library which is easier to use than coding myself? $\endgroup$ Sep 3, 2021 at 7:05

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