How to create a good evaluation function for a game?

Question

I write programs to play board game variants sometimes. The basic strategy is standard alpha-beta pruning or similar searches, sometimes augmented by the usual approaches to

Answer 1

Take in mind that it's not nescessarily true that a decent evaluation function even exists. For this statement I assume that, an evaluation function has to be of low complexity (P).

Answer 2

Find a few candidates for your evaluation function, like mobility (# of possible moves) minus opponent's mobility, then try to find the optimal weight for each metric. Genetic algorithms seem to work pretty well for optimizing weights in an evaluation function.

Create a population with random weights, fight them against each other with a limited depth and turns, replace the losers with random combinations from the winners, shuffle, and repeat, printing out the population average after every generation. Let it run until you're satisfied with the result, or until you see a need to adjust the range for some of the metrics and try again, if it appears that the optimal value for one metric might be outside your initial range.

Late edit: A more accepted, studied, understood approach that I didn't know at the time is something called "Differential Evolution". Offspring are created from 3 parents instead of 2, in such a way that avoids the problem of premature convergence towards the average.