Grouping individuals into families

We have a simulation program where we take a very large population of individual people and group them into families. Each family is then run through the simulation.

I am in charge of grouping the i开发者_如何学JAVAndividuals into families, and I think it is a really cool problem.

Right now, my technique is pretty naive/simple. Each individual record has some characteristics, including married/single, age, gender, and income level. For married people I select an individual and loop through the population and look for a match based on a match function. For people/couples with children I essentially do the same thing, looking for a random number of children (selected according to an empirical distribution) and then loop through all of the children and pick them out and add them to the family based on a match function. After this, not everybody is matched, so I relax the restrictions in my match function and loop through again. I keep doing this, but I stop before my match function gets too ridiculous (marries 85-year-olds to 20-year-olds for example). Anyone who is leftover is written out as a single person.

This works well enough for our current purposes, and I'll probably never get time or permission to rework it, but I at least want to plan for the occasion or learn some cool stuff - even if I never use it. Also, I'm afraid the algorithm will not work very well for smaller sample sizes. Does anybody know what type of algorithms I can study that might relate to this problem or how I might go about formalizing it?

For reference, I'm comfortable with chapters 1-26 of CLRS, but I haven't really touched NP-Completeness or Approximation Algorithms. Not that you shouldn't bring up those topics, but if you do, maybe go easy on me because I probably won't understand everything you are talking about right away. :) I also don't really know anything about evolutionary algorithms.

Edit: I am specifically looking to improve the following:

1. Less ridiculous marriages.
2. Less single people at the end.

Perhaps what you are looking for is cluster analysis?

Lets try to think of your problem like this (starting by solving the spouses matching):
If you were to have a matrix where each row is a male and each column is a female, and every cell in that matrix is the match function's returned value, what you are now looking for is selecting cells so that there won't be a row or a column in which more than one cell is selected, and the total sum of all selected cells should be maximal. This is very similar to the N Queens Problem, with the modification that each allocation of a "queen" has a reward (which we should maximize).
You could solve this problem by using a graph where:
You have a root,
each of the first raw's cells' values is an edge's weight leading to first depth vertices
each of the second raw's cells' values is an edge's weight leading to second depth vertices..
Etc.
(Notice that when you find a match to the first female, you shouldn't consider her anymore, and so for every other female you find a match to) Then finding the maximum allocation can be done by BFS, or better still by A* (notice A* typically looks for minimum cost, so you'll have to modify it a bit).

For matching between couples (or singles, more on that later..) and children, I think KNN with some modifications is your best bet, but you'll need to optimize it to your needs. But now I have to relate to your edit..
How do you measure your algorithm's efficiency?
You need a function that receives the expected distribution of all states (single, married with one children, single with two children, etc.), and the distribution of all states in your solution, and grades the solution accordingly. How do you calculate the expected distribution? That's quite a bit of statistics work..
First you need to know the distribution of all states (single, married.. as mentioned above) in the population,
then you need to know the distribution of ages and genders in the population,
and last thing you need to know - the distribution of ages and genders in your population. Only then, according to those three, can you calculate how many people you expect to be in each state.. And then you can measure the distance between what you expected and what you got... That is a lot of typing.. Sorry for the general parts...