The need to optimize a function whose derivatives are unknown or non-existent arises in many contexts, particularly in real-world applications. Various direct search methods, most notably the Nelder-Mead ?simplex? method, were proposed in the early 1960s for such problems, and have been enormously popular with practitione ever since. Nonetheless, for 25 years these methods were typically dismissed or ignored in the mainstream optimization literature, primarily because of the lack of rigorous convergence results. Since 1989, however, direct search methods have been rejuvenated and made respectable. This talk will cover a (brief) history of direct search methods, recent developments, and future research directions.