APA 6th Edition Jakobović, D. i Golub, M. (1999). Adaptive Genetic Algorithm. Journal of computing and information technology, 7 (3), 229-235. Preuzeto s https://hrcak.srce.hr/150181
MLA 8th Edition Jakobović, Domagoj i Marin Golub. "Adaptive Genetic Algorithm." Journal of computing and information technology, vol. 7, br. 3, 1999, str. 229-235. https://hrcak.srce.hr/150181. Citirano 17.02.2020.
Chicago 17th Edition Jakobović, Domagoj i Marin Golub. "Adaptive Genetic Algorithm." Journal of computing and information technology 7, br. 3 (1999): 229-235. https://hrcak.srce.hr/150181
Harvard Jakobović, D., i Golub, M. (1999). 'Adaptive Genetic Algorithm', Journal of computing and information technology, 7(3), str. 229-235. Preuzeto s: https://hrcak.srce.hr/150181 (Datum pristupa: 17.02.2020.)
Vancouver Jakobović D, Golub M. Adaptive Genetic Algorithm. Journal of computing and information technology [Internet]. 1999 [pristupljeno 17.02.2020.];7(3):229-235. Dostupno na: https://hrcak.srce.hr/150181
IEEE D. Jakobović i M. Golub, "Adaptive Genetic Algorithm", Journal of computing and information technology, vol.7, br. 3, str. 229-235, 1999. [Online]. Dostupno na: https://hrcak.srce.hr/150181. [Citirano: 17.02.2020.]
Sažetak In this paper we introduce an adaptive, 'self-contained' genetic algorithm (GA) with steady-state selection. This variant of GA utilizes empirically based methods for calculating its control parameters. The adaptive algorithm estimates the percentage of the population to be replaced with new individuals (generation gap). It chooses the solutions for crossover and varies the number of mutations, ail regarding the current population state. The state of the population is evaluated by observing some of its characteristic values, such as the best and worst individual's cost function (fitness) values, the population average etc. Furthermore, a non-uniform mutation operator is introduced, which increases the algorithm's efficiency. Adaptive method does not, however, restrict the applicability in any way. The described GA is applied to optimization of several multimodal functions with various degrees of complexity, employed earlier for comparative studies. Some deceptive problems were also taken into consideration, and a comparison between the adaptive and standard genetic algorithm has been made.