A P2P GENETIC ALGORITHM ENVIRONMENT FOR THE INTERNET.
The article presents information on John Holland's ideas about robust adaptive systems that led to the design and implementation of genetic algorithms (GAs). Since then, GAs have been demonstrated to be an effective problem-solving tool for tackling complex optimization and machine learning problems...
| Publicado en: | Communications of the ACM Vol. 48; no. 4; pp. 113 - 117 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Association for Computing Machinery
Apr2005
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=16746248&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 16746248 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Apr2005 vid: 48 iid: 4 pid: 68 pub: Association for Computing Machinery artinfo: ui: 16746248 10.1145/1053291.1053297 ppf: 113 ppct: 4 formats: tig: atl: A P2P GENETIC ALGORITHM ENVIRONMENT FOR THE INTERNET. aug: au: Tan, K. C. Wang, M. L. Peng, W. affil: Associate Professor in the Department of Electrical and Computer Engineering at the National University of Singapore. Software Engineer, BMC Software in Singapore. System Engineer, STMicroelectronics in Singapore. su: Genetic programming Combinatorial optimization User interfaces Genetic algorithms Web development Computer networks sug: subj: Genetic programming Combinatorial optimization User interfaces Genetic algorithms Web development Computer networks ab: The article presents information on John Holland's ideas about robust adaptive systems that led to the design and implementation of genetic algorithms (GAs). Since then, GAs have been demonstrated to be an effective problem-solving tool for tackling complex optimization and machine learning problems. The GA exhibits global search capabilities by simultaneously evaluating performances at multiple points in the solution space. The fundamental approach of reducing computational workload is to develop more efficient algorithms based on a sound theoretical understanding of GAs. The P2P GA environment provides a friendly user interface and a uniform GA executor capable of utilizing the resources of all participating computers on the Internet, while hiding the complexity of network programming from the end users. The P2P GA infrastructure is built on top of a class library that makes it easy to use or to expand with advanced features for solving sophisticated problems posted by the users. The P2P GA framework covers issues such as the choice of migration strategies, the use of communication mechanisms, and the installation of user-defined evaluators. pubtype: Periodical doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2005 holdings: @attributes: islocal: N |
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