Optimization of Discrete Parameters Using the Adaptive Gradient Method and Directed Evolution
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| Format: | Preprint |
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2024
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| _version_ | 1866909072776757248 |
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| author | Beinarovich, Andrei Stepanov, Sergey Zaslavsky, Alexander |
| author_facet | Beinarovich, Andrei Stepanov, Sergey Zaslavsky, Alexander |
| contents | The problem is considered of optimizing discrete parameters in the presence of constraints. We use the stochastic sigmoid with temperature and put forward the new adaptive gradient method CONGA. The search for an optimal solution is carried out by a population of individuals. Each of them varies according to gradients of the 'environment' and is characterized by two temperature parameters with different annealing schedules. Unadapted individuals die, and optimal ones interbreed, the result is directed evolutionary dynamics. The proposed method is illustrated using the well-known combinatorial problem for optimal packing of a backpack (0-1 KP). |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_06834 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimization of Discrete Parameters Using the Adaptive Gradient Method and Directed Evolution Beinarovich, Andrei Stepanov, Sergey Zaslavsky, Alexander Optimization and Control Machine Learning Neural and Evolutionary Computing The problem is considered of optimizing discrete parameters in the presence of constraints. We use the stochastic sigmoid with temperature and put forward the new adaptive gradient method CONGA. The search for an optimal solution is carried out by a population of individuals. Each of them varies according to gradients of the 'environment' and is characterized by two temperature parameters with different annealing schedules. Unadapted individuals die, and optimal ones interbreed, the result is directed evolutionary dynamics. The proposed method is illustrated using the well-known combinatorial problem for optimal packing of a backpack (0-1 KP). |
| title | Optimization of Discrete Parameters Using the Adaptive Gradient Method and Directed Evolution |
| topic | Optimization and Control Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2401.06834 |