COGIN is a system designed for induction of symbolic decision models from pre-classed examples based on the use of genetic algorithms (GAS). Much research in symbolic induction has focused on techniques for reducing classification inaccuracies that arise from inherent limits of underlying incremental search techniques. Genetic Algorithms offer an intriguing alternative to step-wise model construction, relying instead on model evolution through global competition. The difficulty is in providing an effective framework for the GA to be practically applied to complex induction problems. COGIN merges traditional induction concepts with genetic search to provide such a framework, and recent experimental results have demonstrated its advantage relative to basic stepwise inductive approaches. In this paper, we describe the essential elements of the COGIN approach and present a favorable comparison of COGIN results with those produced by a more sophisticated stepwise approach (with support post processing) on standardized multiplexor problems.