12. Randomized Optimization

  1. Dirk P. Kroese1,
  2. Thomas Taimre1 and
  3. Zdravko I. Botev2

Published Online: 20 SEP 2011

DOI: 10.1002/9781118014967.ch12

Handbook of Monte Carlo Methods

Handbook of Monte Carlo Methods

How to Cite

Kroese, D. P., Taimre, T. and Botev, Z. I. (2011) Randomized Optimization, in Handbook of Monte Carlo Methods, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9781118014967.ch12

Author Information

  1. 1

    University of Queensland

  2. 2

    Université de Montréal

Publication History

  1. Published Online: 20 SEP 2011
  2. Published Print: 28 FEB 2011

ISBN Information

Print ISBN: 9780470177938

Online ISBN: 9781118014967



  • cross-entropy method;
  • deterministic problems;
  • evolutionary algorithms;
  • Monte Carlo simulation;
  • noisy optimization problems;
  • randomized optimization;
  • simulated annealing;
  • stochastic approximation;
  • stochastic counterpart method


This chapter discusses optimization methods that have randomness as a core ingredient. Such randomized algorithms can be useful for solving optimization problems with many local optima and complicated constraints, possibly involving a mix of continuous and discrete variables. Randomized algorithms are also used to solve noisy optimization problems, in which the objective function is unknown and has to be obtained via Monte Carlo simulation. The chapter considers randomized optimization methods for both noisy and deterministic problems, including stochastic approximation, the stochastic counterpart method, simulated annealing, evolutionary algorithms, and the cross-entropy method.

Controlled Vocabulary Terms

cross-entropy method; Monte Carlo methods; Stochastic approximation; stochastic processes