International Journal of u- and e- Service, Science and Technology 3.3 Population In genetic algorithm, to mimic natural selection, set the size of the population Individuals have the low fitness score are abandoned to maintain the size of individuals after the operation of genetic algorithm [1]. As the size of the population is increases, the population has more diversity. But it makes the computing time of genetic algorithm increasing. In this paper, we set the number of individual as 10 to generate real-time recommendation and to maintain certain rate of diversity Genetic algorithm creates the initial individual as predefined population randomly [1] However, the individual of this paper reflects the tastes of the user. So 5 individuals are generated randomly, and another 5 individuals are generated based on 3. 4 A Fitness Function In genetic algorithm, a fitness function is used to evaluate the superiority of the each individual and the higher score indicates that the individual is better [1] One of our goal is to identify and adapt the users real-time intent for provide a list of the appropriate recommendation. The information collected in real-time to reflect on the evaluation function for generate more appropriate recommendation. For more information about this will be discussed in Section 4 3.5 Genetic Operations During each successive generation, a proportion of the existing population is selected to breed a new generation. Individual solutions are selected through a fitness-based process, where fitter solutions as measured by a fitness function are typically more likely to be selected [lI In this paper, we choose a roulette wheel selection. A form of fitness-proportionate selection in which the chance of an individual's being selected is proportional to the mount by which its fitness is greater or less than its competitors' fitness Conceptually, this can be represented as a game of roulette-each individual gets a slice of the wheel, but more fit ones get larger slices than less fit ones. The wheel is then spun, and whichever individual owns the section on which it lands each time is In genetic algorithm, crossover operation selects genes from parent chromosomes and creates a new offspring [1]. In this paper, we used uniform crossover because the genes of each individual can be replaced with an equal probability. Each gene is equal to the intersection to be replaced as independently (see Figure 3) Mutation in the genetic algorithm is used for maintaining diversity as a chance to nsert new genes in a group this paper, we set the probability of mutation as 5% and use random exchange method. This mutation method is changed one or more genes randomly at selected individuals3.3 Population In genetic algorithm, to mimic natural selection, set the size of the population. Individuals have the low fitness score are abandoned to maintain the size of individuals after the operation of genetic algorithm [1]. As the size of the population is increases, the population has more diversity. But it makes the computing time of genetic algorithm increasing. In this paper, we set the number of individual as 10 to generate real-time recommendation and to maintain certain rate of diversity. Genetic algorithm creates the initial individual as predefined population randomly [1]. However, the individual of this paper reflects the tastes of the user. So 5 individuals are generated randomly, and another 5 individuals are generated based on existing user's information. 3.4 A Fitness Function In genetic algorithm, a fitness function is used to evaluate the superiority of the each individual and the higher score indicates that the individual is better [1]. One of our goal is to identify and adapt the user's real-time intent for provide a list of the appropriate recommendation. The information collected in real-time to reflect on the evaluation function for generate more appropriate recommendation. For more information about this will be discussed in Section 4. 3.5 Genetic Operations During each successive generation, a proportion of the existing population is selected to breed a new generation. Individual solutions are selected through a fitness-based process, where fitter solutions as measured by a fitness function are typically more likely to be selected [1]. In this paper, we choose a roulette wheel selection. A form of fitness-proportionate selection in which the chance of an individual's being selected is proportional to the amount by which its fitness is greater or less than its competitors' fitness. Conceptually, this can be represented as a game of roulette - each individual gets a slice of the wheel, but more fit ones get larger slices than less fit ones. The wheel is then spun, and whichever individual owns the section on which it lands each time is chosen. In genetic algorithm, crossover operation selects genes from parent chromosomes and creates a new offspring [1]. In this paper, we used uniform crossover because the genes of each individual can be replaced with an equal probability. Each gene is equal to the intersection to be replaced as independently (see Figure 3). Mutation in the genetic algorithm is used for maintaining diversity as a chance to insert new genes in a group [1]. In this paper, we set the probability of mutation as 5%, and use random exchange method. This mutation method is changed one or more genes randomly at selected individuals. 12 International Journal of u- and e- Service, Science and Technology