Bi-objective optimization of nutrient intake and performance of broiler chickens using Gaussian process regression and genetic algorithm
| dc.contributor.author | Ahmadi, Hamed | |
| dc.contributor.author | Rodehutscord, Markus | |
| dc.contributor.author | Siegert, Wolfgang | |
| dc.date.accessioned | 2024-09-03T13:37:58Z | |
| dc.date.available | 2024-09-03T13:37:58Z | |
| dc.date.issued | 2023 | de |
| dc.description.abstract | This study investigated whether quantifying the trade-off between the maxima of two response traits increases the accuracy of diet formulation. To achieve this, average daily weight gain (ADG) and gain:feed ratio (G:F) responses of 7–21-day-old broiler chickens to the dietary supply of three nutrients (intake of digestible glycine equivalents, digestible threonine, and total choline) were modeled using a newly developed hybrid machine learning-based method of Gaussian process regression and genetic algorithm. The dataset comprised 90 data lines. Model-fit-criteria indicated a high model adjustment and no prediction bias of the models. The bi-objective optimization scenarios through Pareto front revealed the trade-off between maximized ADG and maximized G:F and provided information on the needed input of the three nutrients that interact with each other to achieve the trade-off scenarios. The trade-off scenarios followed a nonlinear pattern. This indicated that choosing target values intermediate to maximized ADG and G:F after single-objective optimization is less accurate than feed formulation after quantifying the trade-off. In conclusion, knowledge of the trade-off between maximized ADG and maximized G:F and the needed nutrient inputs will help feed formulators to optimize their feed with a more holistic approach. | en |
| dc.identifier.uri | https://hohpublica.uni-hohenheim.de/handle/123456789/16483 | |
| dc.identifier.uri | https://doi.org/10.3389/fanim.2023.1042725 | |
| dc.language.iso | eng | de |
| dc.rights.license | cc_by | de |
| dc.source | 2673-6225 | de |
| dc.source | ; Vol. 4 (2023) 1042725 | de |
| dc.subject | Gaussian process regression | |
| dc.subject | Genetic algorithm | |
| dc.subject | Machine learning | |
| dc.subject | Broiler chickens | |
| dc.subject | Feed optimization | |
| dc.subject | Multi-objective optimization | |
| dc.subject.ddc | 630 | |
| dc.title | Bi-objective optimization of nutrient intake and performance of broiler chickens using Gaussian process regression and genetic algorithm | en |
| dc.type.dini | Article | |
| dcterms.bibliographicCitation | Frontiers in animal science, 4 (2023), 1042725. https://doi.org/10.3389/fanim.2023.1042725. ISSN: 2673-6225 | |
| dcterms.bibliographicCitation.issn | 2673-6225 | |
| dcterms.bibliographicCitation.journaltitle | Frontiers in animal science | |
| dcterms.bibliographicCitation.volume | 4 | |
| local.export.bibtex | @article{Ahmadi2023, url = {https://hohpublica.uni-hohenheim.de/handle/123456789/16483}, doi = {10.3389/fanim.2023.1042725}, author = {Ahmadi, Hamed and Rodehutscord, Markus and Siegert, Wolfgang et al.}, title = {Bi-objective optimization of nutrient intake and performance of broiler chickens using Gaussian process regression and genetic algorithm}, journal = {Frontiers in animal science}, year = {2023}, volume = {4}, } | |
| local.export.bibtexAuthor | Ahmadi, Hamed and Rodehutscord, Markus and Siegert, Wolfgang et al. | |
| local.export.bibtexKey | Ahmadi2023 | |
| local.export.bibtexType | @article |