AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking
• An adaptive and transfer guided metaheuristic is proposed for Evolutionary Multitasking. • Synergies between tasks are analyzed along the search in a dynamic way. • 4 different combinatorial optimization problems have been considered. • 11 multitasking scenarios are solved comprised by 5 to 20 ins...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Osaba, Eneko [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
Multifactorial evolutionary algorithm |
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| Umfang: |
22 |
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| Übergeordnetes Werk: |
Enthalten in: Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study - Petrruzziello, Carmelina ELSEVIER, 2013, an international journal, New York, NY |
|---|---|
| Übergeordnetes Werk: |
volume:570 ; year:2021 ; pages:577-598 ; extent:22 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.ins.2021.05.005 |
|---|
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ELV054348722 |
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| 520 | |a • An adaptive and transfer guided metaheuristic is proposed for Evolutionary Multitasking. • Synergies between tasks are analyzed along the search in a dynamic way. • 4 different combinatorial optimization problems have been considered. • 11 multitasking scenarios are solved comprised by 5 to 20 instances. • Proposed AT-MFCGA is compared with MFEA, MFEA-II and MFCGA. | ||
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Osaba, Eneko |
| doi_str_mv |
10.1016/j.ins.2021.05.005 |
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| title_sort |
at-mfcga: an adaptive transfer-guided multifactorial cellular genetic algorithm for evolutionary multitasking |
| title_auth |
AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking |
| abstract |
• An adaptive and transfer guided metaheuristic is proposed for Evolutionary Multitasking. • Synergies between tasks are analyzed along the search in a dynamic way. • 4 different combinatorial optimization problems have been considered. • 11 multitasking scenarios are solved comprised by 5 to 20 instances. • Proposed AT-MFCGA is compared with MFEA, MFEA-II and MFCGA. |
| abstractGer |
• An adaptive and transfer guided metaheuristic is proposed for Evolutionary Multitasking. • Synergies between tasks are analyzed along the search in a dynamic way. • 4 different combinatorial optimization problems have been considered. • 11 multitasking scenarios are solved comprised by 5 to 20 instances. • Proposed AT-MFCGA is compared with MFEA, MFEA-II and MFCGA. |
| abstract_unstemmed |
• An adaptive and transfer guided metaheuristic is proposed for Evolutionary Multitasking. • Synergies between tasks are analyzed along the search in a dynamic way. • 4 different combinatorial optimization problems have been considered. • 11 multitasking scenarios are solved comprised by 5 to 20 instances. • Proposed AT-MFCGA is compared with MFEA, MFEA-II and MFCGA. |
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| title_short |
AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking |
| url |
https://doi.org/10.1016/j.ins.2021.05.005 |
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| author2 |
Del Ser, Javier Martinez, Aritz D. Lobo, Jesus L. Herrera, Francisco |
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Del Ser, Javier Martinez, Aritz D. Lobo, Jesus L. Herrera, Francisco |
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| up_date |
2024-07-06T21:28:56.725Z |
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