APA 6th Edition Jakobović, D. & Golub, M. (1999). Adaptive Genetic Algorithm. Journal of computing and information technology, 7 (3), 229-235. Retrieved from https://hrcak.srce.hr/150181
MLA 8th Edition Jakobović, Domagoj and Marin Golub. "Adaptive Genetic Algorithm." Journal of computing and information technology, vol. 7, no. 3, 1999, pp. 229-235. https://hrcak.srce.hr/150181. Accessed 16 Nov. 2019.
Chicago 17th Edition Jakobović, Domagoj and Marin Golub. "Adaptive Genetic Algorithm." Journal of computing and information technology 7, no. 3 (1999): 229-235. https://hrcak.srce.hr/150181
Harvard Jakobović, D., and Golub, M. (1999). 'Adaptive Genetic Algorithm', Journal of computing and information technology, 7(3), pp. 229-235. Available at: https://hrcak.srce.hr/150181 (Accessed 16 November 2019)
Vancouver Jakobović D, Golub M. Adaptive Genetic Algorithm. Journal of computing and information technology [Internet]. 1999 [cited 2019 November 16];7(3):229-235. Available from: https://hrcak.srce.hr/150181
IEEE D. Jakobović and M. Golub, "Adaptive Genetic Algorithm", Journal of computing and information technology, vol.7, no. 3, pp. 229-235, 1999. [Online]. Available: https://hrcak.srce.hr/150181. [Accessed: 16 November 2019]
Abstracts 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.