Illinois Genetic Algorithms Laboratory

Lanzi,P.L,Nichetti,L., Sastry, K., Voltini,D., Goldberg, D. E. (2008)
TR No.: 2008008 | Download PDF | Download PS

Abstract: This paper presents a real-coded estimation distribution algorithm (EDA) inspired to the extended compact genetic algorithm (ECGA) and the real-coded Bayesian Optimization Algorithm (rBOA). Like ECGA, the proposed algorithm partitions the problem variables into a set of clusters that are manipulated as independent variables and estimates the population distribution using marginal product models (MPMs); like rBOA, it employs finite mixtures of models and it does not use any sort of discretization. Accordingly, the proposed real-coded EDA can be either viewed as the extension of the ECGA to real-valued domains by means of finite mixture models or as a simplification of the real-coded BOA to the marginal product models (MPMs). The results reported here show that the number of evaluations required by the proposed algorithm scales sub-quadratically with the problem size in additively separable problems.