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This paper introduces new initialization approaches for evolutionary algorithms that solve two-stage stochastic mixed-integer problems. The two-stage stochastic mixed-integer programs are handled by a stage decomposition based hybrid algorithm where an evolutionary algorithm handles the first-stage decisions and mathematical programming handles the second-stage decisions. The population of the evolutionary algorithm is initialized by using solutions which are generated in a preprocessing step of the hybrid algorithm. This paper presents three different initialization approaches in which the two-stage stochastic mixed-integer program is exploited in order to obtain potentially good starting solutions for the evolutionary algorithm. In case of infeasible initializations, the population is driven toward feasibility by a penalty function. Comparisons of an evolutionary algorithm with a classical random initialization and the new initialization approaches for two real-world problems show that the new initialization approaches lead to high quality feasible solutions in significantly shorter computing times.