Pure adaptive search in global optimization
Mathematical Programming: Series A and B
Pure adaptive search for finite global optimization
Mathematical Programming: Series A and B
Implementing pure adaptive search for global optimization using Markov chain sampling
Journal of Global Optimization
Approximation of the distribution of convergence times for stochastic global optimisation
Journal of Global Optimization
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Backtracking adaptive search is an optimisation algorithm which generalises pure adaptive search and hesitant adaptive search. This paper considers the number of iterations for which the algorithm runs, on a problem with finitely many range levels, in order to reach a sufficiently extreme objective function level. A difference equation for the expectation of this quantity is derived and solved. Several examples of backtracking adaptive search on finite problems are presented, including special cases that have received attention in earlier papers.