Global memory schemes for dynamic optimization
| Autor: | Yesnier Bravo, Gabriel Luque, Enrique Alba |
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| Rok vydání: | 2015 |
| Předmět: |
0209 industrial biotechnology
Focus (computing) Flat memory model Theoretical computer science Computer science Distributed computing Complex system Process (computing) 02 engineering and technology Field (computer science) Computer Science Applications Set (abstract data type) 020901 industrial engineering & automation Theory of computation 0202 electrical engineering electronic engineering information engineering Key (cryptography) 020201 artificial intelligence & image processing |
| Zdroj: | Natural Computing. 15:319-333 |
| ISSN: | 1572-9796 1567-7818 |
| DOI: | 10.1007/s11047-015-9497-2 |
| Popis: | Nowadays, it is common to find research problems (in system biology, mobile applications, etc.) that change over time, requiring algorithms which dynamically adapt the search to the new conditions. In most of them, the utilization of some information from the past allows to quickly adapt after a change. This is the idea underlining the use of memory in this field, what involves key design issues concerning the memory content, the process of update, and the process of retrieval. In this article, we focus on global memory schemes, which are the most intuitive and popular ones, and perform an integral analysis of current design variants based on a comprehensive set of benchmarks. Results show the benefits and drawbacks of each strategy, as well as the effect of the algorithm and problem features in the memory performance. |
| Databáze: | OpenAIRE |
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