Pregled bibliografske jedinice broj: 16055
Adaptive Genetic Algorithm
Adaptive Genetic Algorithm // Proceedings of the 20th International Conference on Information Technology Interfaces / Kalpić, Damir ; Hljuz Dobrić, Vesna (ur.).
Pula: Sveučilišni računski centar Sveučilišta u Zagrebu (Srce), 1998. str. 519-524 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 16055 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Adaptive Genetic Algorithm
Autori
Jakobović, Domagoj ; Golub, Marin
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 20th International Conference on Information Technology Interfaces
/ Kalpić, Damir ; Hljuz Dobrić, Vesna - Pula : Sveučilišni računski centar Sveučilišta u Zagrebu (Srce), 1998, 519-524
Skup
20th Int. Conf. on Information Technology Interfaces, ITI '98
Mjesto i datum
Pula, Hrvatska, 16.06.1998. - 19.06.1998
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
genetic algorithm; local and global optima; adaptive genetic operators
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 percent of the population to be replaced with new individuals (generation gap). It chooses the solutions for crossover and varies the number of mutations, all 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.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo