Multifactorial Evolution: Toward Evolutionary Multitasking
The design of evolutionary algorithms has typically been focused on efficiently solving a single optimization problem at a time. Despite the implicit parallelism of population-based search, no attempt has yet been made to multitask, i.e., to solve multiple optimization problems simultaneously using...
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| Autor*in: |
Abhishek Gupta [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on evolutionary computation - New York, NY : IEEE, 1997, 20(2016), 3, Seite 343-357 |
|---|---|
| Übergeordnetes Werk: |
volume:20 ; year:2016 ; number:3 ; pages:343-357 |
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|---|
| DOI / URN: |
10.1109/TEVC.2015.2458037 |
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OLC1978477775 |
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| 520 | |a The design of evolutionary algorithms has typically been focused on efficiently solving a single optimization problem at a time. Despite the implicit parallelism of population-based search, no attempt has yet been made to multitask, i.e., to solve multiple optimization problems simultaneously using a single population of evolving individuals. Accordingly, this paper introduces evolutionary multitasking as a new paradigm in the field of optimization and evolutionary computation. We first formalize the concept of evolutionary multitasking and then propose an algorithm to handle such problems. The methodology is inspired by biocultural models of multifactorial inheritance , which explain the transmission of complex developmental traits to offspring through the interactions of genetic and cultural factors. Furthermore, we develop a cross-domain optimization platform that allows one to solve diverse problems concurrently. The numerical experiments reveal several potential advantages of implicit genetic transfer in a multitasking environment. Most notably, we discover that the creation and transfer of refined genetic material can often lead to accelerated convergence for a variety of complex optimization functions. | ||
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10.1109/TEVC.2015.2458037 doi PQ20160719 (DE-627)OLC1978477775 (DE-599)GBVOLC1978477775 (PRQ)c1312-d69d0798a4df3820e88c5021071b66430e1ceb5adfb4b978bef6305ecc12e65a0 (KEY)0326032120160000020000300343multifactorialevolutiontowardevolutionarymultitask DE-627 ger DE-627 rakwb eng 620 DNB Abhishek Gupta verfasserin aut Multifactorial Evolution: Toward Evolutionary Multitasking 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The design of evolutionary algorithms has typically been focused on efficiently solving a single optimization problem at a time. Despite the implicit parallelism of population-based search, no attempt has yet been made to multitask, i.e., to solve multiple optimization problems simultaneously using a single population of evolving individuals. Accordingly, this paper introduces evolutionary multitasking as a new paradigm in the field of optimization and evolutionary computation. We first formalize the concept of evolutionary multitasking and then propose an algorithm to handle such problems. The methodology is inspired by biocultural models of multifactorial inheritance , which explain the transmission of complex developmental traits to offspring through the interactions of genetic and cultural factors. Furthermore, we develop a cross-domain optimization platform that allows one to solve diverse problems concurrently. The numerical experiments reveal several potential advantages of implicit genetic transfer in a multitasking environment. Most notably, we discover that the creation and transfer of refined genetic material can often lead to accelerated convergence for a variety of complex optimization functions. Evolutionary Multitasking Genetics Sociology Statistics Multitasking Memetic Computation Cultural differences Evolutionary computation Discrete Optimization Continuous Optimization Optimization Yew-Soon Ong oth Liang Feng oth Enthalten in IEEE transactions on evolutionary computation New York, NY : IEEE, 1997 20(2016), 3, Seite 343-357 (DE-627)230109055 (DE-600)1386081-1 (DE-576)059981148 1089-778X nnns volume:20 year:2016 number:3 pages:343-357 http://dx.doi.org/10.1109/TEVC.2015.2458037 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_30 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_4307 AR 20 2016 3 343-357 |
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10.1109/TEVC.2015.2458037 doi PQ20160719 (DE-627)OLC1978477775 (DE-599)GBVOLC1978477775 (PRQ)c1312-d69d0798a4df3820e88c5021071b66430e1ceb5adfb4b978bef6305ecc12e65a0 (KEY)0326032120160000020000300343multifactorialevolutiontowardevolutionarymultitask DE-627 ger DE-627 rakwb eng 620 DNB Abhishek Gupta verfasserin aut Multifactorial Evolution: Toward Evolutionary Multitasking 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The design of evolutionary algorithms has typically been focused on efficiently solving a single optimization problem at a time. Despite the implicit parallelism of population-based search, no attempt has yet been made to multitask, i.e., to solve multiple optimization problems simultaneously using a single population of evolving individuals. Accordingly, this paper introduces evolutionary multitasking as a new paradigm in the field of optimization and evolutionary computation. We first formalize the concept of evolutionary multitasking and then propose an algorithm to handle such problems. The methodology is inspired by biocultural models of multifactorial inheritance , which explain the transmission of complex developmental traits to offspring through the interactions of genetic and cultural factors. Furthermore, we develop a cross-domain optimization platform that allows one to solve diverse problems concurrently. The numerical experiments reveal several potential advantages of implicit genetic transfer in a multitasking environment. Most notably, we discover that the creation and transfer of refined genetic material can often lead to accelerated convergence for a variety of complex optimization functions. Evolutionary Multitasking Genetics Sociology Statistics Multitasking Memetic Computation Cultural differences Evolutionary computation Discrete Optimization Continuous Optimization Optimization Yew-Soon Ong oth Liang Feng oth Enthalten in IEEE transactions on evolutionary computation New York, NY : IEEE, 1997 20(2016), 3, Seite 343-357 (DE-627)230109055 (DE-600)1386081-1 (DE-576)059981148 1089-778X nnns volume:20 year:2016 number:3 pages:343-357 http://dx.doi.org/10.1109/TEVC.2015.2458037 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_30 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_4307 AR 20 2016 3 343-357 |
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10.1109/TEVC.2015.2458037 doi PQ20160719 (DE-627)OLC1978477775 (DE-599)GBVOLC1978477775 (PRQ)c1312-d69d0798a4df3820e88c5021071b66430e1ceb5adfb4b978bef6305ecc12e65a0 (KEY)0326032120160000020000300343multifactorialevolutiontowardevolutionarymultitask DE-627 ger DE-627 rakwb eng 620 DNB Abhishek Gupta verfasserin aut Multifactorial Evolution: Toward Evolutionary Multitasking 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The design of evolutionary algorithms has typically been focused on efficiently solving a single optimization problem at a time. Despite the implicit parallelism of population-based search, no attempt has yet been made to multitask, i.e., to solve multiple optimization problems simultaneously using a single population of evolving individuals. Accordingly, this paper introduces evolutionary multitasking as a new paradigm in the field of optimization and evolutionary computation. We first formalize the concept of evolutionary multitasking and then propose an algorithm to handle such problems. The methodology is inspired by biocultural models of multifactorial inheritance , which explain the transmission of complex developmental traits to offspring through the interactions of genetic and cultural factors. Furthermore, we develop a cross-domain optimization platform that allows one to solve diverse problems concurrently. The numerical experiments reveal several potential advantages of implicit genetic transfer in a multitasking environment. Most notably, we discover that the creation and transfer of refined genetic material can often lead to accelerated convergence for a variety of complex optimization functions. Evolutionary Multitasking Genetics Sociology Statistics Multitasking Memetic Computation Cultural differences Evolutionary computation Discrete Optimization Continuous Optimization Optimization Yew-Soon Ong oth Liang Feng oth Enthalten in IEEE transactions on evolutionary computation New York, NY : IEEE, 1997 20(2016), 3, Seite 343-357 (DE-627)230109055 (DE-600)1386081-1 (DE-576)059981148 1089-778X nnns volume:20 year:2016 number:3 pages:343-357 http://dx.doi.org/10.1109/TEVC.2015.2458037 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_30 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_4307 AR 20 2016 3 343-357 |
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Abhishek Gupta |
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10.1109/TEVC.2015.2458037 |
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multifactorial evolution: toward evolutionary multitasking |
| title_auth |
Multifactorial Evolution: Toward Evolutionary Multitasking |
| abstract |
The design of evolutionary algorithms has typically been focused on efficiently solving a single optimization problem at a time. Despite the implicit parallelism of population-based search, no attempt has yet been made to multitask, i.e., to solve multiple optimization problems simultaneously using a single population of evolving individuals. Accordingly, this paper introduces evolutionary multitasking as a new paradigm in the field of optimization and evolutionary computation. We first formalize the concept of evolutionary multitasking and then propose an algorithm to handle such problems. The methodology is inspired by biocultural models of multifactorial inheritance , which explain the transmission of complex developmental traits to offspring through the interactions of genetic and cultural factors. Furthermore, we develop a cross-domain optimization platform that allows one to solve diverse problems concurrently. The numerical experiments reveal several potential advantages of implicit genetic transfer in a multitasking environment. Most notably, we discover that the creation and transfer of refined genetic material can often lead to accelerated convergence for a variety of complex optimization functions. |
| abstractGer |
The design of evolutionary algorithms has typically been focused on efficiently solving a single optimization problem at a time. Despite the implicit parallelism of population-based search, no attempt has yet been made to multitask, i.e., to solve multiple optimization problems simultaneously using a single population of evolving individuals. Accordingly, this paper introduces evolutionary multitasking as a new paradigm in the field of optimization and evolutionary computation. We first formalize the concept of evolutionary multitasking and then propose an algorithm to handle such problems. The methodology is inspired by biocultural models of multifactorial inheritance , which explain the transmission of complex developmental traits to offspring through the interactions of genetic and cultural factors. Furthermore, we develop a cross-domain optimization platform that allows one to solve diverse problems concurrently. The numerical experiments reveal several potential advantages of implicit genetic transfer in a multitasking environment. Most notably, we discover that the creation and transfer of refined genetic material can often lead to accelerated convergence for a variety of complex optimization functions. |
| abstract_unstemmed |
The design of evolutionary algorithms has typically been focused on efficiently solving a single optimization problem at a time. Despite the implicit parallelism of population-based search, no attempt has yet been made to multitask, i.e., to solve multiple optimization problems simultaneously using a single population of evolving individuals. Accordingly, this paper introduces evolutionary multitasking as a new paradigm in the field of optimization and evolutionary computation. We first formalize the concept of evolutionary multitasking and then propose an algorithm to handle such problems. The methodology is inspired by biocultural models of multifactorial inheritance , which explain the transmission of complex developmental traits to offspring through the interactions of genetic and cultural factors. Furthermore, we develop a cross-domain optimization platform that allows one to solve diverse problems concurrently. The numerical experiments reveal several potential advantages of implicit genetic transfer in a multitasking environment. Most notably, we discover that the creation and transfer of refined genetic material can often lead to accelerated convergence for a variety of complex optimization functions. |
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| title_short |
Multifactorial Evolution: Toward Evolutionary Multitasking |
| url |
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| author2 |
Yew-Soon Ong Liang Feng |
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Yew-Soon Ong Liang Feng |
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2024-07-03T21:49:03.646Z |
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