A Survey of Some Model-Based Methods for Global Optimization

  • Jiaqiao Hu
  • Yongqiang Wang
  • Enlu Zhou
  • Michael C. Fu
  • Steven I. Marcus
Part of the Systems & Control: Foundations & Applications book series (SCFA)


We review some recent developments of a class of random search methods: model-based methods for global optimization problems. Probability models are used to guide the construction of candidate solutions in model-based methods, which makes them easy to implement and applicable to problems with little structure. We have developed various frameworks for model-based algorithms to guide the updating of probabilistic models and to facilitate convergence proofs. Specific methods covered in this survey include model reference adaptive search, a particle-filtering approach, an evolutionary games approach, and a stochastic approximation-based gradient approach.


Candidate Solution Global Optimal Solution Evolutionary Game Stochastic Approximation Replicator Dynamic 



This work was supported in part by the National Science Foundation (NSF) under Grants CNS-0926194, CMMI-0856256, CMMI-0900332, CMMI-1130273, CMMI-1130761, EECS-0901543, and by the Air Force Office of Scientific Research (AFOSR) under Grant FA9550-10-1-0340.


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Jiaqiao Hu
    • 1
  • Yongqiang Wang
    • 2
  • Enlu Zhou
    • 3
  • Michael C. Fu
    • 4
  • Steven I. Marcus
    • 2
  1. 1.Department of Applied Mathematics and StatisticsState University at Stony BrookStony BrookUSA
  2. 2.Department of Electrical and Computer Engineering & Institute for Systems ResearchUniversity of MarylandCollege ParkUSA
  3. 3.Department of Industrial and Enterprise Systems EngineeringUniversity of Illinois at Urbana-ChampaignChicagoUSA
  4. 4.The Robert H. Smith School of Business & Institute for Systems ResearchUniversity of MarylandCollege ParkUSA

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