Self-Adaptive Genetic Programming for Manufacturing Big Data Analysis
While black-box-based machine learning algorithms have high analytical consistency in manufacturing big data analysis, those algorithms experience difficulties in interpreting the results based on the manufacturing process principle. To overcome this limitation, we present a Self-Adaptive Genetic Pr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/13/4/709 |
| _version_ | 1797537256041349120 |
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| author | Sanghoun Oh Woong-Hyun Suh Chang-Wook Ahn |
| author_facet | Sanghoun Oh Woong-Hyun Suh Chang-Wook Ahn |
| author_sort | Sanghoun Oh |
| collection | DOAJ |
| description | While black-box-based machine learning algorithms have high analytical consistency in manufacturing big data analysis, those algorithms experience difficulties in interpreting the results based on the manufacturing process principle. To overcome this limitation, we present a Self-Adaptive Genetic Programming (SAGP) for manufacturing big data analysis. In Genetic Programming (GP), the solution is expressed as a relationship between variables using mathematical symbols, and the solution with the highest explanatory power is finally selected. These advantages enable intuitive interpretation on manufacturing mechanisms and derive manufacturing principles based on the variables represented by formulas. However, GP occasionally has trouble adjusting the balance between high accuracy and detailed interpretation due to an incommensurable symmetry of the solutions. In order to effectively handle this drawback, we apply the self-adaptive mechanism into GP for managing crossover and mutation probabilities regarding the complexity of tree structure solutions in each generation. Our proposed algorithm showed equal or superior performance compared to other machine learning algorithms. We believe our proposed method can be applied in diverse manufacturing big data analytics in the future. |
| first_indexed | 2024-03-10T12:13:27Z |
| format | Article |
| id | doaj.art-6cba14afd1ff498c86844c3605845c6f |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T12:13:27Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-6cba14afd1ff498c86844c3605845c6f2023-11-21T16:02:32ZengMDPI AGSymmetry2073-89942021-04-0113470910.3390/sym13040709Self-Adaptive Genetic Programming for Manufacturing Big Data AnalysisSanghoun Oh0Woong-Hyun Suh1Chang-Wook Ahn2Department of Computer Science, Korea National Open University, Seoul 03087, KoreaAI Graduate School, Gwangju Institute of Science and Technology, Gwangju 61005, KoreaAI Graduate School, Gwangju Institute of Science and Technology, Gwangju 61005, KoreaWhile black-box-based machine learning algorithms have high analytical consistency in manufacturing big data analysis, those algorithms experience difficulties in interpreting the results based on the manufacturing process principle. To overcome this limitation, we present a Self-Adaptive Genetic Programming (SAGP) for manufacturing big data analysis. In Genetic Programming (GP), the solution is expressed as a relationship between variables using mathematical symbols, and the solution with the highest explanatory power is finally selected. These advantages enable intuitive interpretation on manufacturing mechanisms and derive manufacturing principles based on the variables represented by formulas. However, GP occasionally has trouble adjusting the balance between high accuracy and detailed interpretation due to an incommensurable symmetry of the solutions. In order to effectively handle this drawback, we apply the self-adaptive mechanism into GP for managing crossover and mutation probabilities regarding the complexity of tree structure solutions in each generation. Our proposed algorithm showed equal or superior performance compared to other machine learning algorithms. We believe our proposed method can be applied in diverse manufacturing big data analytics in the future.https://www.mdpi.com/2073-8994/13/4/709manufacturing big data analysisgenetic programmingself-adaptive genetic programming |
| spellingShingle | Sanghoun Oh Woong-Hyun Suh Chang-Wook Ahn Self-Adaptive Genetic Programming for Manufacturing Big Data Analysis Symmetry manufacturing big data analysis genetic programming self-adaptive genetic programming |
| title | Self-Adaptive Genetic Programming for Manufacturing Big Data Analysis |
| title_full | Self-Adaptive Genetic Programming for Manufacturing Big Data Analysis |
| title_fullStr | Self-Adaptive Genetic Programming for Manufacturing Big Data Analysis |
| title_full_unstemmed | Self-Adaptive Genetic Programming for Manufacturing Big Data Analysis |
| title_short | Self-Adaptive Genetic Programming for Manufacturing Big Data Analysis |
| title_sort | self adaptive genetic programming for manufacturing big data analysis |
| topic | manufacturing big data analysis genetic programming self-adaptive genetic programming |
| url | https://www.mdpi.com/2073-8994/13/4/709 |
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