Virtual Savant for the Knapsack Problem: learning for automatic resource allocation
This article presents the application of Virtual Savant to solve resource allocation problems, a widely-studied area with several real-world applications. Virtual Savant is a novel soft computing method that uses machine learning techniques to compute solutions to a given optimization problem. Virtu...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Ivannikov Institute for System Programming of the Russian Academy of Sciences
2019-06-01
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| Series: | Труды Института системного программирования РАН |
| Subjects: | |
| Online Access: | https://ispranproceedings.elpub.ru/jour/article/view/1158 |
| Summary: | This article presents the application of Virtual Savant to solve resource allocation problems, a widely-studied area with several real-world applications. Virtual Savant is a novel soft computing method that uses machine learning techniques to compute solutions to a given optimization problem. Virtual Savant aims at learning how to solve a given problem from the solutions computed by a reference algorithm, and its design allows taking advantage of modern parallel computing infrastructures. The proposed approach is evaluated to solve the Knapsack Problem, which models different variant of resource allocation problems, considering a set of instances with varying size and difficulty. The experimental analysis is performed on an Intel Xeon Phi many-core server. Results indicate that Virtual Savant is able to compute accurate solutions while showing good scalability properties when increasing the number of computing resources used. |
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| ISSN: | 2079-8156 2220-6426 |