SGMIT: using selfish gene theory to construct mutualinformation trees for optimization
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
SGEGC: A Selfish Gene Theory Based Optimization Method by Exchanging Genetic Components
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
IEEE Transactions on Evolutionary Computation
Entropy-based substructural local search for the bayesian optimization algorithm
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Entropy measurement-based estimation model for bayesian optimization algorithm
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Entropy-based evaluation relaxation strategy for Bayesian optimization algorithm
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Estimation of particle swarm distribution algorithms: Combining the benefits of PSO and EDAs
Information Sciences: an International Journal
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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.