NDLI: On the Scalability of Real-Coded Bayesian Optimization Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Chang Wook Ahn Ramakrishna, R.S.
Copyright Year	1997
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 problems 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 of evaluations is computed by multiplying the population size for learning a correct probabilistic model (i.e., population complexity) and the number of generations before convergence, (i.e., convergence time complexity). Experimental results support the scalability model of rBOA. The rBOA shows a subquadratic (in problem size) scalability for uniformly scaled decomposable problems.
Starting Page	307
Ending Page	322
Page Count	16
File Size	721975
File Format	PDF
ISSN	1089778X
Volume Number	12
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Scalability Bayesian methods Electronic design automation and methodology Iterative algorithms Convergence Genetic algorithms Evolutionary computation Couplings Probability distribution Size measurement scalability Decomposable problems estimation of distribution algorithms (EDAs) probabilistic models real-coded Bayesian optimization algorithm (rBOA)
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computational Theory and Mathematics Software

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