A similarity-based cooperative co-evolutionary algorithm for dynamic interval multi-objective optimization problems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Dynamic interval multi-objective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multi-objective cooperative co-evolutionary optimization based on the interval similarity is presented in this paper to handle DI-MOPs. In the framework, a strategy for decomposing decision variables is first proposed, through which all the decision variables are divided into two groups according to the interval similarity between each decision variable and interval parameters. Following that, two sub-populations are utilized to cooperatively optimize decision variables in the two groups. Furthermore, two response strategies, rgb0.00,0.00,0.00i.e., a strategy based on the change intensity and a random mutation strategy, are employed to rapidly track the changing Pareto front of the optimization problem. The proposed algorithm is applied to eight benchmark optimization instances rgb0.00,0.00,0.00as well as a multi-period portfolio selection problem and compared with five state-of-the-art evolutionary algorithms. The experimental results reveal that the proposed algorithm is very competitive on most optimization instances

Fear, anger, and risk preference reversals: An experimental study on a Chinese sample

Fear and anger are basic emotions of the same valence which differ in terms of their certainty and control dimensions according to the Appraisal Tendency Framework, a theory addressing the relationship between specific emotions, and judgments and choices. Past research based on the Appraisal Theory revealed contradictory results for risky choice decision-making. However, these conclusions were drawn from Western samples (e.g., North American). Considering potential cultural differences, the present study aims to investigate whether the Appraisal Tendency hypothesis yields the same results in a Chinese sample. Our first study explores how dispositional fear and anger influence risk preferences through a classic virtual “Asia Disease Problem” task and the second study investigates how induced fear and anger influence risk preferences through an incentive-compatible task. Consistent with previous research, our results reveal that induced fear and anger have differential effects on risky decisions: angry participants prefer the risk-seeking option, whereas fearful participants prefer a risk-averse option. However, we find no associations between dispositional fear (or anger) and risky decisions