On Random Search with a Learning Memory,
Luc Devroye · Defense Technical Information Center (DTIC) · 1976
A new class of random search algorithms for stochastic optimization is presented. The designer has the option to employ a learning memory in order to reduce the cost of the optimization process measured in terms of the number of observations. The asymptotical properties of the procedure are discussed, and new probability theoretical techniques are used in the proof of convergence. (Author)