Whereas the fact that Genetic Algorithms are massively parallel positions them to solve difficult problems by effectively exploiting modern computing technology, the future of genetic search is unclear. Fundamental question concerning GA response remain unanswered, partially because it is not apparent from a theoretical vantage point what GAs are. Recent developments in their formalization via probabilistic techniques are beginning to change this situation. This project is directed at broadening the coherent group of results which forms the most recent and complete theoretical underpinnings of simple GAs to date. Specific objectives include characterizing the emergent behavior of the simple GA as an evolutionary system, utilizing recent probabilistic formalizations to analyze the behavior of evolutionary trajectories in quantitative terms, and developing predictive relationships and principle governing the direction of genetic search.//
