期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2015
卷号：23
期号：4
页码：175-179
DOI：10.14445/22312803/IJCTT-V23P136
出版社：Seventh Sense Research Group
摘要：In recent years, data mining and Genetic algorithms is an essential aspect for searching and generating association rules among the large number of itemsets. Genetic algorithms maintain a population pool of candidate solutions called strings or chromosomes. Each chromosome p is a collection of building blocks known as genes, which are instantiated with values from a finite domain. Associated with each chromosome is a fitness value which is determined by a user defined function, called the fitness function. The performance of a GA is dependent on the genetic operators in general and on the type of crossover operator, in particular. Effective crossover in a GA is achieved through establishing the optimum relationship between the crossover and the search problem itself. In this paper, an preliminary studies have been carried out to enable the researcher to identify the various genetic algorithm methods.
