CAJ | 학술논문

文中结合遗传算法和粒子群优化算法各自的优势，采用协同进化的思想，同时应用两种算法来遍历两个种群，并引入它们的信息交互机制。最后，实验和应用证明，在可接受的时间复杂度的前提下，协同进化算法不但能继承传统遗传算法的优越性，有效地减少扫描数据库的次数，和产生小规模的候选项目集；而且通过比较协同进化算法，传统的遗传算法和粒子群优化算法的属性，在关联规则挖掘中使用该算法，能避免早熟的现象。采取协同进化算法时可以发现高品质的关联规则，尤其是在高维数据库中。
문중결합유전산법화입자군우화산법각자적우세，채용협동진화적사상，동시응용량충산법래편력량개충군，병인입타문적신식교호궤제。최후，실험화응용증명，재가접수적시간복잡도적전제하，협동진화산법불단능계승전통유전산법적우월성，유효지감소소묘수거고적차수，화산생소규모적후선항목집；이차통과비교협동진화산법，전통적유전산법화입자군우화산법적속성，재관련규칙알굴중사용해산법，능피면조숙적현상。채취협동진화산법시가이발현고품질적관련규칙，우기시재고유수거고중。
This paper adopts a co-evolution algorithm, which utilizes improved genetic algorithm and particle swarm optimiza-tion algorithm to iterate two populations simultaneously. Meanwhile, the mechanism of information interaction between these two populations is introduced. Finally, experiments and application have been made to prove that on the premise of acceptable time complexity, not only does the co-evolution algorithm inherit the superiority of traditional genetic algorithm such as reduc-ing the number of scanning the database effectively and generating small-scale candidate item sets, but also avoid the phenome-non of premature through comparing the properties of co-evolution algorithm, traditional genetic algorithm and particle swarm optimization algorithm when used in association rules mining. High quality association rules can be found when adopted the co-evolution algorithm, especially in high-dimension database.