Stochastic Optimization: a Review

By Dimitris Fouskakis, Denise Draper. 2002

211 citations recorded by the source corpus.

Subfields: Emergency Medical Services, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/cd4f3168e26454e182cfd71832ba17e55bd53b8f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Emergency Medical Services

1. Randal Douc
2. Y. Xiang
3. Eric Moulines
4. Jeffrey S. Rosenthal
5. Julia Hoeng
6. Brian P. Suomela
7. Florian Martin
8. Sylvain Gubian

In Management Science and Operations Research

1. A. van der Linde
2. Randal Douc
3. N. G. Best
4. Y. Xiang
5. Eric Moulines
6. Jeffrey S. Rosenthal
7. Julia Hoeng
8. Brian P. Suomela

Citations in the corpus

• Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems (2022, background): “Further readings and descriptive algorithms on SA can be found in Fouskakis and Draper (2002); Suman and Kumar (2006); Eglese (1990).”
• Bayesian measures of model complexity and fit (2002, background): “See Draper and Fouskakis (2000) and Fouskakis and Draper (2002) for an example from health policy that puts this approach into practice, as follows.”
• Generalized Simulated Annealing for Global Optimization: The GenSA Package (2013, methodology): “For a review of stochastic optimization algorithms, please refer to Fouskakis and Draper (2002).”
• Quantitative bounds on convergence of time-inhomogeneous Markov chains (2004, methodology): “In this section, we apply the results above to study the convergence of the simulated annealing (SA) algorithm for continuous global optimization [see Locatelli (2001, 2002), Fouskakis and Draper (2001), Andrieu, Breyer and Doucet (2001) and the references therein].”
• Generalized Simulated Annealing (2017, methodology): “For a more extensive review of stochastic optimization algorithms, see the review provided by Fouskakis and Draper [15].”
• Finding optimal control strategies for invasive species: a density‐structured model for Spartina alterniflora (2004, background): “Genetic algorithms belong to a class of algorithm known as stochastic global optimization (Torn & Viitanen 1999; Fouskakis & Draper 2002 ) that have the philosophy of using randomness in a constructive way to search globally for an optimal solution.”
• Computational optimization techniques applied to microgrids planning: A review (2015, background): “Wright [17] define different categories such as: continue and discrete, constrained and unconstrained, global and local, stochastic [18] and deterministic, multimodal and multiobjective and heuristic and metaheuristic optimization [19].”