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However, it is often impossible to obtain equations that are solutions of the differential models, which makes it difficult to fit the models to the phenomena data. We proposed a method for optimising differential models by applying an evolution strategy algorithm and repeating the numerical integration of the differential model. In this process, the parameter set for the differential model is mutated using a normal distribution and numerically integrated, and a parameter set adapted to the phenomena data is selected. One step of mutation and selection is regarded as one generation, and an appropriate parameter is sought by repeating the process for an arbitrary number of generations. To evaluate this method, we applied the Gompertz model to cow growth data and the Wood model to lactation milk yield data. We compared the results applying the Gauss\u2013Newton method to both models directly with those obtained with our method applied to the differential equations of both models. As a result, by implementing this method for 30,000 generations, both models had similar parameter values with the same accuracy as those fitted by the Gauss\u2013Newton method. This suggests that our method is effective for parameter estimation in differential models. Note, however, that the parameter estimation accuracy in this method is affected by the number of generations and the fluctuation range of the normal distribution used for parameter mutation.", "subitem_description_language": "en", "subitem_description_type": "Abstract"}]}, "item_7_publisher_7": {"attribute_name": "\u51fa\u7248\u8005", "attribute_value_mlt": [{"subitem_publisher": "\u65b0\u6f5f\u5927\u5b66\u8fb2\u5b66\u90e8", "subitem_publisher_language": "ja"}]}, "item_7_source_id_11": {"attribute_name": "\u66f8\u8a8c\u30ec\u30b3\u30fc\u30c9ID", "attribute_value_mlt": [{"subitem_source_identifier": "AN00183393", "subitem_source_identifier_type": "NCID"}]}, "item_7_source_id_9": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "0385-8634", "subitem_source_identifier_type": "PISSN"}]}, "item_access_right": {"attribute_name": "\u30a2\u30af\u30bb\u30b9\u6a29", "attribute_value_mlt": [{"subitem_access_right": "open access", "subitem_access_right_uri": 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