Comparative study of regression vs pairwise models for surrogate-based heuristic optimisation
Heuristic optimisation is a popular tool for solving problems in the sciences and engineering fields. These algorithms explore the search space by sampling solutions, evaluating their fitness, and biasing the search in the direction of promising solutions. However, in many cases, this fitness functi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Naharro, Pablo S. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Development and Initial Validation of the Pain Resilience Scale - Slepian, P. Maxwell ELSEVIER, 2016transfer abstract, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:75 ; year:2022 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.swevo.2022.101176 |
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ELV05959361X |
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| 520 | |a Heuristic optimisation is a popular tool for solving problems in the sciences and engineering fields. These algorithms explore the search space by sampling solutions, evaluating their fitness, and biasing the search in the direction of promising solutions. However, in many cases, this fitness function involves executing expensive computational calculations, drastically reducing the reasonable number of evaluations. In this context, surrogate models have emerged as an excellent alternative to alleviate these computational problems. This paper addresses the formulation of surrogate problems as both regression models that approximate fitness (surface surrogate models) and a novel way to connect classification models (pairwise surrogate models). The pairwise approach can be directly exploited by some algorithms, such as Differential Evolution, in which the fitness value is not actually needed to drive the search, and it is sufficient to know whether a solution is better than another one or not. | ||
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10.1016/j.swevo.2022.101176 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001973.pica (DE-627)ELV05959361X (ELSEVIER)S2210-6502(22)00143-2 DE-627 ger DE-627 rakwb eng 610 VZ 620 VZ 52.20 bkl 50.32 bkl 50.25 bkl Naharro, Pablo S. verfasserin aut Comparative study of regression vs pairwise models for surrogate-based heuristic optimisation 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Heuristic optimisation is a popular tool for solving problems in the sciences and engineering fields. These algorithms explore the search space by sampling solutions, evaluating their fitness, and biasing the search in the direction of promising solutions. However, in many cases, this fitness function involves executing expensive computational calculations, drastically reducing the reasonable number of evaluations. In this context, surrogate models have emerged as an excellent alternative to alleviate these computational problems. This paper addresses the formulation of surrogate problems as both regression models that approximate fitness (surface surrogate models) and a novel way to connect classification models (pairwise surrogate models). The pairwise approach can be directly exploited by some algorithms, such as Differential Evolution, in which the fitness value is not actually needed to drive the search, and it is sufficient to know whether a solution is better than another one or not. Heuristic optimisation is a popular tool for solving problems in the sciences and engineering fields. These algorithms explore the search space by sampling solutions, evaluating their fitness, and biasing the search in the direction of promising solutions. However, in many cases, this fitness function involves executing expensive computational calculations, drastically reducing the reasonable number of evaluations. In this context, surrogate models have emerged as an excellent alternative to alleviate these computational problems. This paper addresses the formulation of surrogate problems as both regression models that approximate fitness (surface surrogate models) and a novel way to connect classification models (pairwise surrogate models). The pairwise approach can be directly exploited by some algorithms, such as Differential Evolution, in which the fitness value is not actually needed to drive the search, and it is sufficient to know whether a solution is better than another one or not. Surrogate-assisted evolutionary algorithms (SAEA) Elsevier Expensive optimisation Elsevier Classifier surrogate models Elsevier Online surrogate models Elsevier Toharia, Pablo oth LaTorre, Antonio oth Peña, José-María oth Enthalten in Elsevier Slepian, P. Maxwell ELSEVIER Development and Initial Validation of the Pain Resilience Scale 2016transfer abstract Amsterdam [u.a.] (DE-627)ELV024261270 volume:75 year:2022 pages:0 https://doi.org/10.1016/j.swevo.2022.101176 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 52.20 Antriebstechnik Getriebelehre VZ 50.32 Dynamik Schwingungslehre Technische Mechanik VZ 50.25 Robotertechnik VZ AR 75 2022 0 |
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10.1016/j.swevo.2022.101176 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001973.pica (DE-627)ELV05959361X (ELSEVIER)S2210-6502(22)00143-2 DE-627 ger DE-627 rakwb eng 610 VZ 620 VZ 52.20 bkl 50.32 bkl 50.25 bkl Naharro, Pablo S. verfasserin aut Comparative study of regression vs pairwise models for surrogate-based heuristic optimisation 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Heuristic optimisation is a popular tool for solving problems in the sciences and engineering fields. These algorithms explore the search space by sampling solutions, evaluating their fitness, and biasing the search in the direction of promising solutions. However, in many cases, this fitness function involves executing expensive computational calculations, drastically reducing the reasonable number of evaluations. In this context, surrogate models have emerged as an excellent alternative to alleviate these computational problems. This paper addresses the formulation of surrogate problems as both regression models that approximate fitness (surface surrogate models) and a novel way to connect classification models (pairwise surrogate models). The pairwise approach can be directly exploited by some algorithms, such as Differential Evolution, in which the fitness value is not actually needed to drive the search, and it is sufficient to know whether a solution is better than another one or not. Heuristic optimisation is a popular tool for solving problems in the sciences and engineering fields. These algorithms explore the search space by sampling solutions, evaluating their fitness, and biasing the search in the direction of promising solutions. However, in many cases, this fitness function involves executing expensive computational calculations, drastically reducing the reasonable number of evaluations. In this context, surrogate models have emerged as an excellent alternative to alleviate these computational problems. This paper addresses the formulation of surrogate problems as both regression models that approximate fitness (surface surrogate models) and a novel way to connect classification models (pairwise surrogate models). The pairwise approach can be directly exploited by some algorithms, such as Differential Evolution, in which the fitness value is not actually needed to drive the search, and it is sufficient to know whether a solution is better than another one or not. Surrogate-assisted evolutionary algorithms (SAEA) Elsevier Expensive optimisation Elsevier Classifier surrogate models Elsevier Online surrogate models Elsevier Toharia, Pablo oth LaTorre, Antonio oth Peña, José-María oth Enthalten in Elsevier Slepian, P. Maxwell ELSEVIER Development and Initial Validation of the Pain Resilience Scale 2016transfer abstract Amsterdam [u.a.] (DE-627)ELV024261270 volume:75 year:2022 pages:0 https://doi.org/10.1016/j.swevo.2022.101176 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 52.20 Antriebstechnik Getriebelehre VZ 50.32 Dynamik Schwingungslehre Technische Mechanik VZ 50.25 Robotertechnik VZ AR 75 2022 0 |
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10.1016/j.swevo.2022.101176 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001973.pica (DE-627)ELV05959361X (ELSEVIER)S2210-6502(22)00143-2 DE-627 ger DE-627 rakwb eng 610 VZ 620 VZ 52.20 bkl 50.32 bkl 50.25 bkl Naharro, Pablo S. verfasserin aut Comparative study of regression vs pairwise models for surrogate-based heuristic optimisation 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Heuristic optimisation is a popular tool for solving problems in the sciences and engineering fields. These algorithms explore the search space by sampling solutions, evaluating their fitness, and biasing the search in the direction of promising solutions. However, in many cases, this fitness function involves executing expensive computational calculations, drastically reducing the reasonable number of evaluations. In this context, surrogate models have emerged as an excellent alternative to alleviate these computational problems. This paper addresses the formulation of surrogate problems as both regression models that approximate fitness (surface surrogate models) and a novel way to connect classification models (pairwise surrogate models). The pairwise approach can be directly exploited by some algorithms, such as Differential Evolution, in which the fitness value is not actually needed to drive the search, and it is sufficient to know whether a solution is better than another one or not. Heuristic optimisation is a popular tool for solving problems in the sciences and engineering fields. These algorithms explore the search space by sampling solutions, evaluating their fitness, and biasing the search in the direction of promising solutions. However, in many cases, this fitness function involves executing expensive computational calculations, drastically reducing the reasonable number of evaluations. In this context, surrogate models have emerged as an excellent alternative to alleviate these computational problems. This paper addresses the formulation of surrogate problems as both regression models that approximate fitness (surface surrogate models) and a novel way to connect classification models (pairwise surrogate models). The pairwise approach can be directly exploited by some algorithms, such as Differential Evolution, in which the fitness value is not actually needed to drive the search, and it is sufficient to know whether a solution is better than another one or not. Surrogate-assisted evolutionary algorithms (SAEA) Elsevier Expensive optimisation Elsevier Classifier surrogate models Elsevier Online surrogate models Elsevier Toharia, Pablo oth LaTorre, Antonio oth Peña, José-María oth Enthalten in Elsevier Slepian, P. Maxwell ELSEVIER Development and Initial Validation of the Pain Resilience Scale 2016transfer abstract Amsterdam [u.a.] (DE-627)ELV024261270 volume:75 year:2022 pages:0 https://doi.org/10.1016/j.swevo.2022.101176 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 52.20 Antriebstechnik Getriebelehre VZ 50.32 Dynamik Schwingungslehre Technische Mechanik VZ 50.25 Robotertechnik VZ AR 75 2022 0 |
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610 VZ 620 VZ 52.20 bkl 50.32 bkl 50.25 bkl Comparative study of regression vs pairwise models for surrogate-based heuristic optimisation Surrogate-assisted evolutionary algorithms (SAEA) Elsevier Expensive optimisation Elsevier Classifier surrogate models Elsevier Online surrogate models Elsevier |
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Development and Initial Validation of the Pain Resilience Scale |
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Development and Initial Validation of the Pain Resilience Scale |
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Development and Initial Validation of the Pain Resilience Scale |
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comparative study of regression vs pairwise models for surrogate-based heuristic optimisation |
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Comparative study of regression vs pairwise models for surrogate-based heuristic optimisation |
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Heuristic optimisation is a popular tool for solving problems in the sciences and engineering fields. These algorithms explore the search space by sampling solutions, evaluating their fitness, and biasing the search in the direction of promising solutions. However, in many cases, this fitness function involves executing expensive computational calculations, drastically reducing the reasonable number of evaluations. In this context, surrogate models have emerged as an excellent alternative to alleviate these computational problems. This paper addresses the formulation of surrogate problems as both regression models that approximate fitness (surface surrogate models) and a novel way to connect classification models (pairwise surrogate models). The pairwise approach can be directly exploited by some algorithms, such as Differential Evolution, in which the fitness value is not actually needed to drive the search, and it is sufficient to know whether a solution is better than another one or not. |
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Heuristic optimisation is a popular tool for solving problems in the sciences and engineering fields. These algorithms explore the search space by sampling solutions, evaluating their fitness, and biasing the search in the direction of promising solutions. However, in many cases, this fitness function involves executing expensive computational calculations, drastically reducing the reasonable number of evaluations. In this context, surrogate models have emerged as an excellent alternative to alleviate these computational problems. This paper addresses the formulation of surrogate problems as both regression models that approximate fitness (surface surrogate models) and a novel way to connect classification models (pairwise surrogate models). The pairwise approach can be directly exploited by some algorithms, such as Differential Evolution, in which the fitness value is not actually needed to drive the search, and it is sufficient to know whether a solution is better than another one or not. |
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Heuristic optimisation is a popular tool for solving problems in the sciences and engineering fields. These algorithms explore the search space by sampling solutions, evaluating their fitness, and biasing the search in the direction of promising solutions. However, in many cases, this fitness function involves executing expensive computational calculations, drastically reducing the reasonable number of evaluations. In this context, surrogate models have emerged as an excellent alternative to alleviate these computational problems. This paper addresses the formulation of surrogate problems as both regression models that approximate fitness (surface surrogate models) and a novel way to connect classification models (pairwise surrogate models). The pairwise approach can be directly exploited by some algorithms, such as Differential Evolution, in which the fitness value is not actually needed to drive the search, and it is sufficient to know whether a solution is better than another one or not. |
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Comparative study of regression vs pairwise models for surrogate-based heuristic optimisation |
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Toharia, Pablo LaTorre, Antonio Peña, José-María |
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