Approximation of the distribution of convergence times for stochastic global optimisation

G. R. Wood*, D. L. Alexander, D. W. Bulger

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


How long should we run a stochastic global optimisation algorithm such as simulated annealing? How should we tune such an algorithm? This paper proposes an approach to the study of these questions through successive approximation of a generic stochastic global optimisation algorithm with a sequence of stochastic processes, culminating in a backtracking adaptive search process. Our emerging understanding of backtracking adaptive search can thus be used to study the original algorithm. The first approximation, the averaged range process, has the same expected number of iterations to convergence as the original process.

Original languageEnglish
Article numberA016
Pages (from-to)271-284
Number of pages14
JournalJournal of Global Optimization
Issue number1-4
Publication statusPublished - 2002
Externally publishedYes


  • Adaptive search
  • Markov chain
  • Optimization
  • Stochastic approximation
  • Stochastic process


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