Customizable execution environments for evolutionary computation using BOINC + virtualization
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| Опубликовано в:: | Natural Computing vol. 12, no. 2 (Jun 2013), p. 163 |
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| Другие авторы: | , , |
| Опубликовано: |
Springer Nature B.V.
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| Online-ссылка: | Citation/Abstract Full Text Full Text - PDF |
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MARC
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| 024 | 7 | |a 10.1007/s11047-012-9343-8 |2 doi | |
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| 045 | 2 | |b d20130601 |b d20130630 | |
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| 100 | 1 | |a Fernández De Vega, Francisco | |
| 245 | 1 | |a Customizable execution environments for evolutionary computation using BOINC + virtualization | |
| 260 | |b Springer Nature B.V. |c Jun 2013 | ||
| 513 | |a Feature | ||
| 520 | 3 | |a Issue Title: Part 1: Special Issue: Distributed Evolutionary Computation in Informal Environments Part 2: Special Issue: DNA Computing 17 Evolutionary algorithms (EAs) consume large amounts of computational resources, particularly when they are used to solve real-world problems that require complex fitness evaluations. Beside the lack of resources, scientists face another problem: the absence of the required expertise to adapt applications for parallel and distributed computing models. Moreover, the computing power of PCs is frequently underused at institutions, as desktops are usually devoted to administrative tasks. Therefore, the proposal in this work consists of providing a framework that allows researchers to massively deploy EA experiments by exploiting the computing power of their instituions' PCs by setting up a Desktop Grid System based on the BOINC middleware. This paper presents a new model for running unmodified applications within BOINC with a web-based centralized management system for available resources. Thanks to this proposal, researchers can run scientific applications without modifying the application's source code, and at the same time manage thousands of computers from a single web page. Summarizing, this model allows the creation of on-demand customized execution environments within BOINC that can be used to harness unused computational resources for complex computational experiments, such as EAs. To show the performance of this model, a real-world application of Genetic Programming was used and tested through a centrally-managed desktop grid infrastructure. Results show the feasibility of the approach that has allowed researchers to generate new solutions by means of an easy to use and manage distributed system.[PUBLICATION ABSTRACT] | |
| 653 | |a Optimization algorithms | ||
| 653 | |a Computer science | ||
| 653 | |a Evolution | ||
| 653 | |a Computer programming | ||
| 700 | 1 | |a Olague, Gustavo | |
| 700 | 1 | |a Trujillo, Leonardo | |
| 700 | 1 | |a Lombraña González, Daniel | |
| 773 | 0 | |t Natural Computing |g vol. 12, no. 2 (Jun 2013), p. 163 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/1355329674/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/1355329674/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/1355329674/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |