Converting decision problems to optimization problems? (evolutionary algorithms)

Question

Decision problems are not suited for use in evolutionary algorithms since a simple right/wrong fitness measure cannot be optimized/evolved. So, what are some methods/techniques for converting decision problems to optimization problems?

For instance, I'm currently working on a problem where the fitness of an individual depends very heavily on the output it produces. Depending on the ordering of genes, an individual either produces no output or perfect output - no "in between" (and therefore, no hills to climb). One small change in an individual's gene ordering can have a drastic effect on the fitness of an individual, so using an evolutionary algorithm essentially amounts to a random search.

Some literature references would be nice if you know of any.

Answer 1

Application to multiple inputs and examination of percentage of correct answers.

True, a right/wrong fitness measure cannot evolve towards more rightness, but an algorithm can nonetheless apply a mutable function to whatever input it takes to produce a decision which will be right or wrong. So, you keep mutating the algorithm, and for each mutated version of the algorithm you apply it to, say, 100 different inputs, and you check how many of them it got right. Then, you select those algorithms that gave more correct answers than others. Who knows, eventually you might see one which gets them all right.

There are no literature references, I just came up with it.