期刊名称：Journal of Emerging Trends in Computing and Information Sciences
电子版ISSN：2079-8407
出版年度：2011
卷号：2
期号：10
页码：498-508
出版社：ARPN Publishers
摘要：This work investigates the performance of two Evolutionary Algorithms Genetic Algorithm and Memetic Algorithm for Constrained Optimization Problem. In particular, a knapsack problem was solved using the two algorithms and their results were compared. Two selection techniques were used for both algorithms. The results of comparative analysis show that Roulette-Wheel selection method outperforms Ranking and scaling method by 4.1% in term of the accuracy of the optimal results obtained. Furthermore, Memetic Algorithm converges faster than Genetic Algorithm even as it also produces more optimal results than Genetic Algorithm produces by a factor of 4.9% when the results obtained from Roulette Wheel selection were compared for both algorithms. It is however pertinent to state that the time taken by an iteration in Genetic Algorithm is 35.9% less than the time taken by an iteration in Memetic Algorithm.
