Багатофакторний конвергенційно-націлений оператор для генетичного алгоритму
Optimization of complex particle transport simulation packages could be managed using genetic algorithms as a tuning instrument for learning statistics and behavior of multi-objective optimisation functions. Combination of genetic algorithm and unsupervised machine learning could significantly incre...
Збережено в:
| Дата: | 2017 |
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| Автори: | , , |
| Формат: | Стаття |
| Мова: | Англійська |
| Опубліковано: |
The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"
2017
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| Теми: | |
| Онлайн доступ: | http://journal.iasa.kpi.ua/article/view/101845 |
| Теги: |
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| Назва журналу: | System research and information technologies |
Репозитарії
System research and information technologies| Резюме: | Optimization of complex particle transport simulation packages could be managed using genetic algorithms as a tuning instrument for learning statistics and behavior of multi-objective optimisation functions. Combination of genetic algorithm and unsupervised machine learning could significantly increase convergence of algorithm to true Pareto Front (PF). We tried to apply specific multivariate analysis operator that can be used in case of expensive fitness function evaluations, in order to speed-up the convergence of the "black-box" optimization problem. The results delivered in the article shows that current approach could be used for any type of genetic algorithm and deployed as a separate genetic operator. |
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