Adaptive optimization algorithms with a priori uncertainty

The basic evolution stages of theory of optimization under uncertainty are discussed. Adaptive optimization algorithms and ways to optimize the algorithms themselves are given. Instability of optimal algorithms is noted and possibilities to eliminate that instability by using a priori data on both the observations and on the solution are discussed. Ways and perspectives of further theory evolution are outlined.