Convergence rates of stochastic global optimisation algorithms
by
David Alexander
Massey University

The unknown convergence rate of a general stochastic global optimisation algorithm is sought by comparison with similar algorithms whose convergence is understood. A new stochastic process is defined based on progress in the range of the optimisation algorithm. This new process remains closely tied to the original algorithm operating in the search domain. However, since the definition is based in the range, it may also be linked to a second range algorithm, a generalisation of Pure Adaptive Search (PAS). The number of iterations to convergence of PAS is fully understood. Analysis of these two derived processes is expected to yield convergence information about the original optimisation algorithm.

Date received: October 5, 2000

Copyright © 2000 by the author(s). The author(s) of this document and the organizers of the conference have granted their consent to include this abstract in Atlas Conferences Inc. Document # caek-53.