Multi-objective optimization by Machine Learning
| dc.contributor.author | Hagstrand, Hampus | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för data och informationsteknik | sv |
| dc.contributor.department | Chalmers University of Technology / Department of Computer Science and Engineering | en |
| dc.contributor.examiner | Bernardy, Jean-Philippe | |
| dc.contributor.supervisor | Seger, Carl-Johan | |
| dc.date.accessioned | 2023-12-22T10:17:40Z | |
| dc.date.available | 2023-12-22T10:17:40Z | |
| dc.date.issued | 2023 | |
| dc.date.submitted | 2023 | |
| dc.description.abstract | Solving multi-objective optimization with machine learning can significantly improve various fields, such as multi-junction traffic management or stock portfolio optimization. These are problems that can have a large amount of relevant and irrelevant data. This thesis targets one such problem area, focusing on multi-objective optimization in trot horse harness racing, specifically the V75. A large part of the project was data-related, such as data collection, preprocessing, and engineering. The predicting part is divided into two parts single race prediction and system predictions. The single-race prediction utilizes the large amount of data collected to train a neural network to predict the percentage of the horse finishing behind the winner. The system prediction uses the result from the neural network to pick a system. During this process, a greedy algorithm selects more horses in the races that the machine learning deems close and fewer that it deems one-sided. The performance evaluation showed that the single race predicting performed on par with the more advanced baseline and showed clear signs of finding a pattern between the data and the finishing result. The system prediction found some accuracy but did not surpass the odds baseline. | |
| dc.identifier.coursecode | DATX05 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12380/307477 | |
| dc.language.iso | eng | |
| dc.setspec.uppsok | Technology | |
| dc.subject | Machine Learning | |
| dc.subject | Artificial Intelligence | |
| dc.subject | Multi-objective Optimization | |
| dc.subject | Horse Racing | |
| dc.subject | V75 | |
| dc.title | Multi-objective optimization by Machine Learning | |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.degree | Master's Thesis | en |
| dc.type.uppsok | H | |
| local.programme | Computer science – algorithms, languages and logic (MPALG), MSc |