Parents have an influence on the formation of their children’s mate preferences. This research conducted two studies to test the relationship between parents’ education level and the gender role characteristics of ideal mate for college students, and the moderating role of urban-rural residence on this relationship. In study 1, 1,033 participants reported their explicit attitude toward gender role characteristics for an ideal mate via the Chinese Sex Role Inventory-50. In study 2, we recruited 130 participants and used an implicit association (...) test to measure their implicit attitude. Regression-based analyses showed that the higher education level of parents was significantly associated with female students’ mate preferences with high-femininity but low-masculinity traits. For male students, the higher education level of parents was associated with their explicit preferences of mates with high-masculinity but low-femininity traits. The significant moderating effect of urban-rural residence was observed in explicit preference, with the different patterns in gender groups. In conclusion, parents with higher educational attainment might bring up children who are more likely to embrace a partner with non-traditional gender roles. (shrink)

In multimodal multiobjective optimization problems, multiple Pareto optimal sets, even some good local Pareto optimal sets, should be reserved, which can provide more choices for decision-makers. To solve MMOPs, this paper proposes an evolutionary algorithm with clustering-based assisted selection strategy for multimodal multiobjective optimization, in which the addition operator and deletion operator are proposed to comprehensively consider the diversity in both decision and objective spaces. Specifically, in decision space, the union population is partitioned into multiple clusters by using a density-based (...) clustering method, aiming to assist the addition operator to strengthen the population diversity. Then, a number of weight vectors are adopted to divide population into N subregions in objective space. Moreover, in the deletion operator, the solutions in the most crowded subregion are first collected into previous clusters, and then the worst solution in the most crowded cluster is deleted until there are N solutions left. Our algorithm is compared with other multimodal multiobjective evolutionary algorithms on the well-known benchmark MMOPs. Numerical experiments report the effectiveness and advantages of our proposed algorithm. (shrink)