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iii Many problems encountered in computer science are best stated in terms of interactions amongst individuals. For example, many problems are most naturally phrased in terms of finding a candidate solution which performs best against a set of test cases. In such situations, methods are needed to find candidate solutions which are expected to perform best over all test cases. Coevolution holds the promise of addressing such problems by employing prin-ciples from biological evolution, where populations of candidate solutions and test cases are evolved over time to produce higher quality solutions. Coevolution has had both success stories as well as noted deficiencies, and many additions to the base coevolutionary algorithm have been proposed to address some of the studied defi-ciences; however, to the author’s knowledge, all such additions have been based on and applied to an underlying evolutionary model. This thesis presents a generaliza-tion of coevolution to Co-Optimization, where optimization techniques that do no