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Multilevel selection theory views natural selection as hierarchy process that acts on any level of biological organizations whenever there exist heritable variation in fitness among units of that level. In this paper, selection schemes of evolutionary algorithms (EAs) are reconsidered from the point of view of the theory, and a novel constraint handling method is introduced in which a two-level selection process, namely within-group selection and between-group selection, is modeled to keep right balance between objective and penalty functions. The method is implemented on 3 group selection models that possessing different population structures and tested using (μ, λ)-evolution strategies on a set of 13 benchmark problems. We show that a proper understanding of multilevel selection theory will help us to design EAs and might also enable us to challenge the old problems from a new angle.