A Model Reference Adaptive Search Method for Global Optimization PDF slides
Steve Marcus, Professor
Univ. Maryland, Department of Electrical and Computer Engineering, Institute for Systems Research

10/22/10, 2:30 PM, GHC-7101


Model Reference Adaptive Search (MRAS) is a randomized search method for solving global optimization problems.  The method works with a parameterized probabilistic model on the solution space and generates at each iteration a group of candidate solutions.  These candidate solutions are then used to update the parameters associated with the probabilistic model in such a way that the future search will be biased toward the region containing high quality solutions.  The parameter updating procedure in MRAS is guided by a sequence of implicit probabilistic models called reference models.  We show that the model reference framework can be used to describe the recently proposed cross-entropy (CE) method for optimization and study its properties.  We prove global convergence of the proposed algorithm in both continuous and combinatorial domains, and we carry out numerical studies to illustrate the performance of the algorithm.  At the end of the talk, we will describe recent work (with Enlu Zhou) utilizing particle filtering to develop a related class of global optimization algorithms.

(Joint work with Jiaqiao Hu and Michael Fu)


Steve Marcus received the B.A. degree in electrical engineering and mathematics from Rice University S.M. and Ph.D. degrees in electrical engineering from MIT.  From 1975 to 1991, he was with the Department of Electrical and Computer Engineering at the University of Texas at Austin.  In 1991, he joined the University of Maryland, College Park, as Professor in the Electrical and Computer Engineering Department and the Institute for Systems Research.  He was Director of the Institute for Systems Research from 1991 to 1996 and Chair of the Electrical and Computer Engineering Department from 2000 to 2005.  Currently, his research is focused on stochastic control, estimation, hybrid systems, and optimization.  He is past Editor-in-Chief of the SIAM Journal on Control and Optimization, and he is a Fellow of SIAM and IEEE.

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nsfSupported by an Expeditions in Computing award from the National Science Foundation