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This paper describes and analyzes population size management, which can be used to enhance the efficiency of the extended compact genetic algorithm (ECGA). The ECGA is a selectorecombinative algorithm that requires an adequate sampling to generate a high-quality model of the problem. Population size management decreases the overall running time of the optimization process by splitting the algorithm into two phases: first, it builds a high-quality model of the problem using a large population; second, it generates a smaller population, sampled using the high-quality model, and performs the remaining of the optimization with a reduced population size. The paper shows that for decomposable optimization problems, population size management leads to a significant optimization speedup that decreases the number of evaluations for convergence in ECGA by a factor of 30% to 70% keeping the same accuracy and reliability. Furthermore, the ECGA using PSM presents the same scalability model as the ECGA.