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Summary: IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. 12, NO. 3, JUNE 2008 307
On the Scalability of Real-Coded Bayesian
Optimization Algorithm
Chang Wook Ahn, Member, IEEE, and R. S. Ramakrishna, Senior Member, IEEE
Abstract--Estimation of distribution algorithms (EDAs) are
major tools in evolutionary optimization. They have the ability to
uncover the hidden regularities of problems and then exploit them
for effective search. Real-coded Bayesian optimization algorithm
(rBOA) which brings the power of discrete BOA to bear upon the
continuous domain has been regarded as a milestone in the field
of numerical optimization. It has been empirically observed that
the rBOA solves, with subquadratic scaleup behavior, numerical
optimization problems of bounded difficulty. This underlines the
scalability of rBOA (at least) in practice. However, there is no firm
theoretical basis for this scalability.
The aim of this paper is to carry out a theoretical analysis of the
scalability of rBOA in the context of additively decomposable prob-
lems with real-valued variables. The scalability is measured by the
growth of the number of fitness function evaluations (in order to
reach the optimum) with the size of the problem. The total number
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