Deep Learning-Based Step Size Determination for Hill Climbing Metaheuristics
Machine Learning-assisted metaheuristics is a new and promising research topic, combining the advantages of both method families. Metaheuristics are widely used general problem solvers that can be fine-tuned by prior knowledge about the search space; however, this adaptation can be a very time-consu...
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MDPI AG
2025-05-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/18/5/298 |
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| author | Sándor Szénási Gábor Légrádi Gábor Kovács |
| author_facet | Sándor Szénási Gábor Légrádi Gábor Kovács |
| author_sort | Sándor Szénási |
| collection | DOAJ |
| description | Machine Learning-assisted metaheuristics is a new and promising research topic, combining the advantages of both method families. Metaheuristics are widely used general problem solvers that can be fine-tuned by prior knowledge about the search space; however, this adaptation can be a very time-consuming and complex task. This paper proposes a hybrid variation of the Hill Climbing method using a Machine Learning model to learn this domain-specific knowledge in advance to help determine the optimal step size of each iteration. A Deep Feedforward Neural Network was trained on the steps of thousands of Hill Climbing runs. This model was used in a novel alternating method (using traditional and Machine Learning-based steps) to predict the optimal step size for each iteration. This hybrid algorithm was compared to the already-known variants. The results show that the novel hybrid method is able to find slightly better results than the original Hill Climbing method, requiring significantly fewer fitness calculations. |
| format | Article |
| id | doaj-art-0bb22bafd1a34631bbdaadb613594281 |
| institution | OA Journals |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-0bb22bafd1a34631bbdaadb6135942812025-08-20T01:56:57ZengMDPI AGAlgorithms1999-48932025-05-0118529810.3390/a18050298Deep Learning-Based Step Size Determination for Hill Climbing MetaheuristicsSándor Szénási0Gábor Légrádi1Gábor Kovács2John von Neumann Faculty of Informatics, Óbuda University, 1034 Budapest, HungaryJohn von Neumann Faculty of Informatics, Óbuda University, 1034 Budapest, HungaryJohn von Neumann Faculty of Informatics, Óbuda University, 1034 Budapest, HungaryMachine Learning-assisted metaheuristics is a new and promising research topic, combining the advantages of both method families. Metaheuristics are widely used general problem solvers that can be fine-tuned by prior knowledge about the search space; however, this adaptation can be a very time-consuming and complex task. This paper proposes a hybrid variation of the Hill Climbing method using a Machine Learning model to learn this domain-specific knowledge in advance to help determine the optimal step size of each iteration. A Deep Feedforward Neural Network was trained on the steps of thousands of Hill Climbing runs. This model was used in a novel alternating method (using traditional and Machine Learning-based steps) to predict the optimal step size for each iteration. This hybrid algorithm was compared to the already-known variants. The results show that the novel hybrid method is able to find slightly better results than the original Hill Climbing method, requiring significantly fewer fitness calculations.https://www.mdpi.com/1999-4893/18/5/298machine learningmetaheuristicshill climbingoptimizationoptimal step-sizeepsilon value |
| spellingShingle | Sándor Szénási Gábor Légrádi Gábor Kovács Deep Learning-Based Step Size Determination for Hill Climbing Metaheuristics Algorithms machine learning metaheuristics hill climbing optimization optimal step-size epsilon value |
| title | Deep Learning-Based Step Size Determination for Hill Climbing Metaheuristics |
| title_full | Deep Learning-Based Step Size Determination for Hill Climbing Metaheuristics |
| title_fullStr | Deep Learning-Based Step Size Determination for Hill Climbing Metaheuristics |
| title_full_unstemmed | Deep Learning-Based Step Size Determination for Hill Climbing Metaheuristics |
| title_short | Deep Learning-Based Step Size Determination for Hill Climbing Metaheuristics |
| title_sort | deep learning based step size determination for hill climbing metaheuristics |
| topic | machine learning metaheuristics hill climbing optimization optimal step-size epsilon value |
| url | https://www.mdpi.com/1999-4893/18/5/298 |
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