dc.contributor.author | Ramanauskas, Mikalojus | |
dc.contributor.author | Šešok, Dmitrij | |
dc.contributor.author | Belevičius, Rimantas | |
dc.contributor.author | Kurilov, Jevgenij | |
dc.contributor.author | Valentinavičius, Saulius | |
dc.date.accessioned | 2023-09-18T16:47:16Z | |
dc.date.available | 2023-09-18T16:47:16Z | |
dc.date.issued | 2017 | |
dc.identifier.issn | 1841-9836 | |
dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/116764 | |
dc.description.abstract | Modified genetic algorithm with special phenotypes’ selection and crossover operators with default specified rules is proposed in this paper thus refusing the random crossover. The suggested crossover operator enables wide distribution of genes of the best phenotypes over the whole population. During selection and crossover, the best phenotypes of the newest population and additionally the genes of the best individuals of two previous populations are involved. The effectiveness of the modified algorithm is shown numerically on the real-life global optimization problem from civil engineering - the optimal pile placement problem under grillage-type foundations. This problem is a fair indicator for global optimization algorithms since the ideal solutions are known in advance but with unknown magnitudes of design parameters. Comparison of the proposed algorithm with 6 other stochastic optimization algorithms clearly reveals its advantages: at similar accuracy level the algorithm requires less time for tuning of genetic parameters and provides narrower confidence intervals on the results than other algorithms. | eng |
dc.format | PDF | |
dc.format.extent | p. 393-402 | |
dc.format.medium | tekstas / txt | |
dc.language.iso | eng | |
dc.relation.isreferencedby | Science Citation Index Expanded (Web of Science) | |
dc.relation.isreferencedby | Scopus | |
dc.relation.isreferencedby | Applied Science & Technology Source | |
dc.relation.isreferencedby | Computers & Applied Sciences Complete | |
dc.source.uri | http://dx.doi.org/10.15837/ijccc.2017.3.2813 | |
dc.subject | IK01 - Informacinės technologijos, ontologinės ir telematikos sistemos / Information technologies, ontological and telematic systems | |
dc.title | Genetic algorithm with modified crossover for grillage optimization | |
dc.type | Straipsnis Web of Science DB / Article in Web of Science DB | |
dcterms.references | 16 | |
dc.type.pubtype | S1 - Straipsnis Web of Science DB / Web of Science DB article | |
dc.contributor.institution | Vilniaus Gedimino technikos universitetas | |
dc.contributor.institution | Vilniaus universitetas Vilniaus Gedimino technikos universitetas | |
dc.contributor.faculty | Fundamentinių mokslų fakultetas / Faculty of Fundamental Sciences | |
dc.subject.researchfield | T 007 - Informatikos inžinerija / Informatics engineering | |
dc.subject.ltspecializations | L103 - Įtrauki ir kūrybinga visuomenė / Inclusive and creative society | |
dc.subject.en | genetic algorithm | |
dc.subject.en | crossover operator | |
dc.subject.en | grillage optimization | |
dcterms.sourcetitle | International journal of computers, communications and control | |
dc.description.issue | no 3 | |
dc.description.volume | Vol. 12 | |
dc.publisher.name | CCC Publications | |
dc.publisher.city | Bihor | |
dc.identifier.doi | 10.15837/ijccc.2017.3.2813 | |
dc.identifier.elaba | 21315633 | |