Zelda Zabinsky (UW, Dept. of Industrial & Systems Engineering)

PDL C401
Abstract: Complex systems are often modeled with blackbox functions that are illstructured and are available only through computer programs. Random search algorithms (e.g., simulated annealing, crossentropy) have been shown to be very effective at making use of probability distributions to focus samples on “good” regions. We analyze an idealized adaptive random search algorithm as a marked Poisson process, and use it to motivate sampling distributions. Then we apply a Markov chain Monte Carlo sampler, namely HitandRun, to approximate the sampling distribution. Recent work extends the analysis to noisy functions, and motivates a partitionbased random search optimization algorithm.