Numerous multi-objective decision-making problems related to industrial process control engineering such as control and operation performance evaluation are being resolved through human-computer interactions.With regard to the problems that traditional interactive evolutionary computing approaches suffer i.e.,limited searching ability and human s strong subjectivity in multi-objective-attribute decision-making,a novel affective computing and learning solution adapted to human-computer interaction mechanism is explicitly proposed.Therein,a kind of stimulating response based affective computing model(STAM) is constructed,along with quantitative relations between affective space and human s subjective preferences.Thereafter,affective learning strategies based on genetic algorithms are introduced which are responsible for gradually grasping essentials in human s subjective judgments in decision-making,reducing human s subjective fatigue as well as making the decisions more objective and scientific.Affective learning algorithm s complexity and convergence analysis are shown in Appendices A and B.To exemplify applications of the proposed methods,ad-hoc test functions and PID parameter tuning are suggested as case studies,giving rise to satisfying results and showing validity of the contributions. Numerous multi-objective decision-making problems related to industrial process control engineering such as control and operation performance evaluation are being through human-computer interactions. Due regard to the problems that traditional interactive evolutionary computing ideas suffer ie, limited searching ability and human s strong subjectivity in multi-objective-attribute decision-making, a novel affective computing and learning solution adapted to human-computer interaction mechanism is explicitly proposed. Here, a kind of stimulating response based affective computing model (STAM) is constructed, along with quantitative relations between affective space and human s subjective preferences.Thereafter, affective learning strategies based on genetic algorithms are introduced which are responsible for gradually grasping essentials in human s subjective judgments in decision-making, reducing human s subjective fatigue as well as making the decisions more objective and sc ientific.Affective learning algorithm s complexity and convergence analysis are shown in Appendices A and B. To exemplify applications of the proposed methods, ad-hoc test functions and PID parameter tuning are suggested as case studies, giving rise to satisfying results and showing validity of the contributions.