A massively parallel architecture for distributed genetic algorithms
Genetic algorithms are a group of stochastic search algorithms with a broad field of application. Although highly successful in many fields, genetic algorithms in general suffer from long execution times. In this article we describe parallel models for genetic algorithms in general and the massively...
Saved in:
| Published in: | Parallel computing Vol. 30; no. 5; pp. 647 - 676 |
|---|---|
| Main Author: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01.05.2004
|
| Subjects: | |
| ISSN: | 0167-8191, 1872-7336 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Genetic algorithms are a group of stochastic search algorithms with a broad field of application. Although highly successful in many fields, genetic algorithms in general suffer from long execution times. In this article we describe parallel models for genetic algorithms in general and the massively parallel Diffusion Model in particular, in order to speedup the execution.
Implemented in hardware, the Diffusion Model constitutes an efficient, flexible, scalable and mobile machine learning system. This fine-grained system consists of a large number of processing nodes that evolve a large number of small, overlapping subpopulations. Every processing node has an embedded CPU that executes a linear machine code representation at a rate of up to 20,000 generations per second.
Besides being efficient, implemented in hardware this model is highly portable and applicable to mobile, on-line applications. The architecture is also scalable so that larger problems can be addressed with a system with more processing nodes. Finally, the use of linear machine code as genetic programming representation and VHDL as hardware description language, makes the system highly flexible and easy to adapt to different applications.
Through a series of experiments we determine the settings of the most important parameters of the Diffusion Model. We also demonstrate the effectiveness and flexibility of the architecture on a set of regression problems, a classification application and a time series forecasting application. |
|---|---|
| AbstractList | Genetic algorithms are a group of stochastic search algorithms with a broad field of application. Although highly successful in many fields, genetic algorithms in general suffer from long execution times. In this article we describe parallel models for genetic algorithms in general and the massively parallel Diffusion Model in particular, in order to speedup the execution.
Implemented in hardware, the Diffusion Model constitutes an efficient, flexible, scalable and mobile machine learning system. This fine-grained system consists of a large number of processing nodes that evolve a large number of small, overlapping subpopulations. Every processing node has an embedded CPU that executes a linear machine code representation at a rate of up to 20,000 generations per second.
Besides being efficient, implemented in hardware this model is highly portable and applicable to mobile, on-line applications. The architecture is also scalable so that larger problems can be addressed with a system with more processing nodes. Finally, the use of linear machine code as genetic programming representation and VHDL as hardware description language, makes the system highly flexible and easy to adapt to different applications.
Through a series of experiments we determine the settings of the most important parameters of the Diffusion Model. We also demonstrate the effectiveness and flexibility of the architecture on a set of regression problems, a classification application and a time series forecasting application. |
| Author | Eklund, Sven E. |
| Author_xml | – sequence: 1 givenname: Sven E. surname: Eklund fullname: Eklund, Sven E. email: sek@du.se organization: Dalarna University, S-781 88 Borlange, Sweden |
| BookMark | eNqFkLluAjEURa0okQIkX5DGPzATL7N4ihSIrBJSGnrLyxswGsbINkj8fUxIlSKpXvPOke6ZouvRj4DQAyUlJbR53JZ7FYwvGSG8pKwkpLtCEypaVrScN9dokr_aQtCO3qJpjFtCSFMJMkHPc7xTMbojDCecJWoYYMBZtnEJTDoEwL0P2LqYgtOHBBavYYTkDFbD2geXNrt4h256NUS4_7kztHp9WS3ei-Xn28diviwMJzQVqm-ZbhvbaAqq1rWoBOO15g0oa1XVQVUx4C10tVWC67rS2lbGsJoTobjgM8QvWhN8jAF6uQ9up8JJUiLPHeRWfneQ5w6SMpk7ZKr7RRmXVHJ-TEG54R_26cJCXnV0EGQ0DkYD1oVcR1rv_uS_ADP-fiY |
| CitedBy_id | crossref_primary_10_1007_s10898_012_9946_8 crossref_primary_10_1108_03684920910973180 crossref_primary_10_1016_j_parco_2014_04_008 crossref_primary_10_1016_j_ejor_2024_01_017 crossref_primary_10_1016_j_jpdc_2006_04_017 crossref_primary_10_1057_jors_2009_161 crossref_primary_10_1108_03684920810907823 crossref_primary_10_3390_s22062389 crossref_primary_10_1007_s00500_013_1140_5 crossref_primary_10_1016_j_engappai_2006_06_015 crossref_primary_10_1007_s00339_024_07657_7 crossref_primary_10_1016_j_cageo_2005_10_015 crossref_primary_10_1007_s00477_012_0649_y crossref_primary_10_1002_fld_1288 crossref_primary_10_1016_j_jpdc_2012_09_011 |
| Cites_doi | 10.1109/CEC.1999.782540 10.1109/EH.1999.785431 10.1007/3-540-45443-8_19 10.1109/CEC.2002.1007025 10.1142/S0129065790000102 10.1109/TC.1972.5009071 |
| ContentType | Journal Article |
| Copyright | 2004 Elsevier B.V. |
| Copyright_xml | – notice: 2004 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.parco.2003.12.009 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1872-7336 |
| EndPage | 676 |
| ExternalDocumentID | 10_1016_j_parco_2003_12_009 S0167819104000365 |
| GroupedDBID | --K --M -~X .DC .~1 0R~ 123 1B1 1~. 1~5 29O 4.4 457 4G. 5VS 6OB 7-5 71M 8P~ 9JN AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABEFU ABFNM ABJNI ABMAC ABXDB ABYKQ ACDAQ ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q G8K GBLVA GBOLZ HLZ HVGLF HZ~ H~9 IHE J1W JJJVA KOM LG9 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SBC SCC SDF SDG SDP SES SEW SPC SPCBC SST SSV SSZ T5K WH7 WUQ XPP ZMT ~G- 9DU AATTM AAXKI AAYWO AAYXX ABDPE ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD |
| ID | FETCH-LOGICAL-c301t-af72b76d6b1ea5b5848235b36eadda49e442e37e95da83b54bbd4cc25308a383 |
| ISICitedReferencesCount | 30 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000222103700007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0167-8191 |
| IngestDate | Sat Nov 29 04:06:53 EST 2025 Tue Nov 18 21:15:45 EST 2025 Fri Feb 23 02:30:43 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 5 |
| Keywords | FPGA Genetic programming Classification Time series forecasting Regression Parallel architecture Diffusion model |
| Language | English |
| License | https://www.elsevier.com/tdm/userlicense/1.0 |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c301t-af72b76d6b1ea5b5848235b36eadda49e442e37e95da83b54bbd4cc25308a383 |
| PageCount | 30 |
| ParticipantIDs | crossref_primary_10_1016_j_parco_2003_12_009 crossref_citationtrail_10_1016_j_parco_2003_12_009 elsevier_sciencedirect_doi_10_1016_j_parco_2003_12_009 |
| PublicationCentury | 2000 |
| PublicationDate | 2004-05-01 |
| PublicationDateYYYYMMDD | 2004-05-01 |
| PublicationDate_xml | – month: 05 year: 2004 text: 2004-05-01 day: 01 |
| PublicationDecade | 2000 |
| PublicationTitle | Parallel computing |
| PublicationYear | 2004 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | R. Tanese, Distributed genetic algorithm, Proceedings of Third International Conference on Genetic algorithms, 1989, pp. 434–439 M. Hutter, Fitness uniform selection to preserve genetic diversity, Proceedings of the 2002 Congress on Evolutionary Computation (CEC-2002), 2002, pp. 783–788 W. Banzhaf, P. Nordin, R. Keller, F. Francone, Genetic Programming––An Introduction, Morgan Kaufmann Publishers Inc, San Francisco and dpunkt Verlag, Heidelberg, 1998, pp. 330–334 (ISBN 1-55860-510-X) P. Nordin, F. Hoffmann, F. Francone, M. Brameier, W. Banzhaf, AIM-GP and parallelism, Proceedings of the Congress on Evolutionary Computation, 1999, pp. 1059–1066 Flynn (BIB10) 1972; C-21 D. Abramson, J. Abela, A parallel genetic algorithm for solving the school timetabling problem, in: Proceedings of the Fifteenth Australian Computer Science Conference (ACSC-15) 14, 1992, pp. 1–11 Tong (BIB22) 1995 S. Baluja, A Massively Distributed Parallel Genetic Algorithm (mdpGA), CMU-CS-92-196R, Carnegie Mellon University, Pittsburgh, Pennsylvania, 1992 S. Eklund, A massively parallel GP architecture, in: K. Giannakoglou, D. Tsahalis, J. Périaux, K. Papailiou, T. Fogarty (Eds.), Evolutionary Methods for Design, Optimization and Control, 2002 (ISBN 84-89925-97-6) Kung, Leiserson (BIB16) 1979 J. Sarma, K. DeJong, On decentralizing selection algorithms, Proceedings of the Sixth International Conference on Genetic Algorithms, Morgan Kaufmann, 1995, pp. 17–23 G. Tufte, P. Haddow, Prototyping a GA pipeline for complete hardware evolution, First NASA/DoD Workshop on Evolvable Hardware, IEEE, 1999 E. Cantú-Paz, A survey of parallel genetic algorithms, Department of Computer Science, Illinois Genetic Algorithms Laboratory, University of Illinois, Urbana-Champaign, 1998 J. Kyngäs, J. Hakkarainen, Predicting sunspot numbers with evolutionary optimized neural networks, in: J. Alander (Ed. ), Proceedings of the Second Nordic Workshop on Genetic Algorithms and their Applications, 1996, pp. 173–180 E. Cantú-Paz, Designing efficient master–slave parallel genetic algorithms, IlliGAL Report No. 97004, University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana, IL, 1997 Holland (BIB12) 1975 Schwehm (BIB20) 1996 Koza (BIB14) 1992 J.J. Grefenstette, Parallel adaptive algorithms for function optimization, Tech. Rep. No. CS-81-19, Vanderbilt University, Computer Science Department, Nashville, TN, 1981 Weigend, Huberman, Rumelhart (BIB24) 1990; 1 D. Abramson, G. Mills, S. Perkins, Parallelization of a genetic algorithm for the computation of efficient train schedules, Proceedings of the 1993 Parallel Computing and Transputers Conference, 1993, pp. 139–149 A.D. Bethke, Comparison of genetic algorithms and gradient-based optimizers on parallel processors: efficiency of use of processing capacity, Tech. Rep. No. 197, University of Michigan, Logic of Computers Group, Ann Arbor, MI, 1976 J. Koza, F. Bennett III, J. Shipman, O. Stiffelman, Building a parallel computer system for $18,000 that performs a half peta-flop per day, Proceedings of the Genetic and Evolutionary Computation Conference, 1999, pp. 1484–1490 S. Eklund, Massively parallel architecture for linear machine code genetic programming, Proceedings of the 2001 International Conference on Evolutionary Systems, Tokyo, 2001 10.1016/j.parco.2003.12.009_BIB15 10.1016/j.parco.2003.12.009_BIB18 10.1016/j.parco.2003.12.009_BIB17 10.1016/j.parco.2003.12.009_BIB19 10.1016/j.parco.2003.12.009_BIB5 Holland (10.1016/j.parco.2003.12.009_BIB12) 1975 Tong (10.1016/j.parco.2003.12.009_BIB22) 1995 10.1016/j.parco.2003.12.009_BIB4 10.1016/j.parco.2003.12.009_BIB7 10.1016/j.parco.2003.12.009_BIB21 10.1016/j.parco.2003.12.009_BIB6 10.1016/j.parco.2003.12.009_BIB9 10.1016/j.parco.2003.12.009_BIB23 10.1016/j.parco.2003.12.009_BIB8 10.1016/j.parco.2003.12.009_BIB11 Koza (10.1016/j.parco.2003.12.009_BIB14) 1992 Flynn (10.1016/j.parco.2003.12.009_BIB10) 1972; C-21 10.1016/j.parco.2003.12.009_BIB13 Weigend (10.1016/j.parco.2003.12.009_BIB24) 1990; 1 10.1016/j.parco.2003.12.009_BIB1 10.1016/j.parco.2003.12.009_BIB3 10.1016/j.parco.2003.12.009_BIB2 Kung (10.1016/j.parco.2003.12.009_BIB16) 1979 Schwehm (10.1016/j.parco.2003.12.009_BIB20) 1996 |
| References_xml | – reference: R. Tanese, Distributed genetic algorithm, Proceedings of Third International Conference on Genetic algorithms, 1989, pp. 434–439 – reference: P. Nordin, F. Hoffmann, F. Francone, M. Brameier, W. Banzhaf, AIM-GP and parallelism, Proceedings of the Congress on Evolutionary Computation, 1999, pp. 1059–1066 – year: 1996 ident: BIB20 article-title: Parallel Population Models for Genetic Algorithms – year: 1975 ident: BIB12 article-title: Adaptation in Natural and Artificial Systems – year: 1995 ident: BIB22 publication-title: Non-linear Time Series: A Dynamical System Approach – reference: W. Banzhaf, P. Nordin, R. Keller, F. Francone, Genetic Programming––An Introduction, Morgan Kaufmann Publishers Inc, San Francisco and dpunkt Verlag, Heidelberg, 1998, pp. 330–334 (ISBN 1-55860-510-X) – reference: J. Kyngäs, J. Hakkarainen, Predicting sunspot numbers with evolutionary optimized neural networks, in: J. Alander (Ed. ), Proceedings of the Second Nordic Workshop on Genetic Algorithms and their Applications, 1996, pp. 173–180 – year: 1992 ident: BIB14 article-title: Genetic Programming: On the Programming of Computers by Means of Natural Selection – reference: S. Eklund, A massively parallel GP architecture, in: K. Giannakoglou, D. Tsahalis, J. Périaux, K. Papailiou, T. Fogarty (Eds.), Evolutionary Methods for Design, Optimization and Control, 2002 (ISBN 84-89925-97-6) – volume: 1 start-page: 193 year: 1990 end-page: 209 ident: BIB24 article-title: Predicting the future: A connectionist approach publication-title: International Journal of Neural Systems – year: 1979 ident: BIB16 article-title: Systolic arrays (for VLSI) publication-title: Sparse Matrix Proceedings – reference: D. Abramson, J. Abela, A parallel genetic algorithm for solving the school timetabling problem, in: Proceedings of the Fifteenth Australian Computer Science Conference (ACSC-15) 14, 1992, pp. 1–11 – reference: M. Hutter, Fitness uniform selection to preserve genetic diversity, Proceedings of the 2002 Congress on Evolutionary Computation (CEC-2002), 2002, pp. 783–788 – reference: J. Koza, F. Bennett III, J. Shipman, O. Stiffelman, Building a parallel computer system for $18,000 that performs a half peta-flop per day, Proceedings of the Genetic and Evolutionary Computation Conference, 1999, pp. 1484–1490 – reference: A.D. Bethke, Comparison of genetic algorithms and gradient-based optimizers on parallel processors: efficiency of use of processing capacity, Tech. Rep. No. 197, University of Michigan, Logic of Computers Group, Ann Arbor, MI, 1976 – reference: S. Baluja, A Massively Distributed Parallel Genetic Algorithm (mdpGA), CMU-CS-92-196R, Carnegie Mellon University, Pittsburgh, Pennsylvania, 1992 – reference: D. Abramson, G. Mills, S. Perkins, Parallelization of a genetic algorithm for the computation of efficient train schedules, Proceedings of the 1993 Parallel Computing and Transputers Conference, 1993, pp. 139–149 – reference: E. Cantú-Paz, A survey of parallel genetic algorithms, Department of Computer Science, Illinois Genetic Algorithms Laboratory, University of Illinois, Urbana-Champaign, 1998 – reference: J. Sarma, K. DeJong, On decentralizing selection algorithms, Proceedings of the Sixth International Conference on Genetic Algorithms, Morgan Kaufmann, 1995, pp. 17–23 – reference: J.J. Grefenstette, Parallel adaptive algorithms for function optimization, Tech. Rep. No. CS-81-19, Vanderbilt University, Computer Science Department, Nashville, TN, 1981 – volume: C-21 start-page: 948 year: 1972 end-page: 960 ident: BIB10 article-title: Some computer organizations and their effectiveness publication-title: IEEE Transactions on Computers – reference: G. Tufte, P. Haddow, Prototyping a GA pipeline for complete hardware evolution, First NASA/DoD Workshop on Evolvable Hardware, IEEE, 1999 – reference: E. Cantú-Paz, Designing efficient master–slave parallel genetic algorithms, IlliGAL Report No. 97004, University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana, IL, 1997 – reference: S. Eklund, Massively parallel architecture for linear machine code genetic programming, Proceedings of the 2001 International Conference on Evolutionary Systems, Tokyo, 2001 – ident: 10.1016/j.parco.2003.12.009_BIB18 doi: 10.1109/CEC.1999.782540 – ident: 10.1016/j.parco.2003.12.009_BIB1 – ident: 10.1016/j.parco.2003.12.009_BIB23 doi: 10.1109/EH.1999.785431 – year: 1975 ident: 10.1016/j.parco.2003.12.009_BIB12 – ident: 10.1016/j.parco.2003.12.009_BIB19 – ident: 10.1016/j.parco.2003.12.009_BIB17 – ident: 10.1016/j.parco.2003.12.009_BIB11 – ident: 10.1016/j.parco.2003.12.009_BIB9 doi: 10.1007/3-540-45443-8_19 – year: 1995 ident: 10.1016/j.parco.2003.12.009_BIB22 – ident: 10.1016/j.parco.2003.12.009_BIB8 – ident: 10.1016/j.parco.2003.12.009_BIB7 – ident: 10.1016/j.parco.2003.12.009_BIB13 doi: 10.1109/CEC.2002.1007025 – ident: 10.1016/j.parco.2003.12.009_BIB15 – ident: 10.1016/j.parco.2003.12.009_BIB6 – year: 1979 ident: 10.1016/j.parco.2003.12.009_BIB16 article-title: Systolic arrays (for VLSI) – ident: 10.1016/j.parco.2003.12.009_BIB3 – ident: 10.1016/j.parco.2003.12.009_BIB2 – year: 1996 ident: 10.1016/j.parco.2003.12.009_BIB20 – ident: 10.1016/j.parco.2003.12.009_BIB21 – ident: 10.1016/j.parco.2003.12.009_BIB4 – volume: 1 start-page: 193 year: 1990 ident: 10.1016/j.parco.2003.12.009_BIB24 article-title: Predicting the future: A connectionist approach publication-title: International Journal of Neural Systems doi: 10.1142/S0129065790000102 – ident: 10.1016/j.parco.2003.12.009_BIB5 – volume: C-21 start-page: 948 year: 1972 ident: 10.1016/j.parco.2003.12.009_BIB10 article-title: Some computer organizations and their effectiveness publication-title: IEEE Transactions on Computers doi: 10.1109/TC.1972.5009071 – year: 1992 ident: 10.1016/j.parco.2003.12.009_BIB14 |
| SSID | ssj0006480 |
| Score | 1.8275653 |
| Snippet | Genetic algorithms are a group of stochastic search algorithms with a broad field of application. Although highly successful in many fields, genetic algorithms... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 647 |
| SubjectTerms | Classification Diffusion model FPGA Genetic programming Parallel architecture Regression Time series forecasting |
| Title | A massively parallel architecture for distributed genetic algorithms |
| URI | https://dx.doi.org/10.1016/j.parco.2003.12.009 |
| Volume | 30 |
| WOSCitedRecordID | wos000222103700007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-7336 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0006480 issn: 0167-8191 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9NAEF5By4ELb0R5aQ_cgqvY-z5GEAQcqkrNITdrX4aU1K3SFPXnM_tyUgVVgMTFiqzsxp7P-WY8OzsfQu8EV0xp1lWsq2lFnasrXTtVubGRHaHc1o5GsQlxdCTnc3WcNdwvo5yA6Ht5fa0u_ivUcA7ADltn_wLuYVI4AZ8BdDgC7HD8I-AnozMIiIHEQt5Cr4JWynJ0Y70gVBa60DA3aF1BwAlT-di3dfntfLVYf88NzHPIelzmsFEAori6EIX_WF6lvPQJUOZoengjhUA3BXspr7WztyWlGoFCw-tc8hSJHqWAeJykliWFP_O6ymJ7hTqSIU-9NLNf5UnnZYeyU_bg9BAsYuNuTBLzs2O18VBD3eBJuKhwTYF6wPeyu2i_EUwBne1PvkznXwcnzGkUzRtuojSciqV9Oz_1-6BkK9CYPUIP8hsCniRkH6M7vn-CHhb1DZzJ-Cn6OMED0LgAjbeBxgA03gIaZ6DxBuhnaPZpOvvwucqaGJUFKl5XuhONEdxxU3vNDISPsiHMEA6M4DRVntLGE-EVc1oSw6gxjlrbMDKWmkjyHO31571_gXCtrPWaik7JjipjtZAwATGm0Y6MdXOAmmKT1uZ-8UG2ZNmWwsDTNhoyKJmStm5aMOQBej8MukjtUm7_Oi_GbnPElyK5Fp6O2wa-_NeBr9D9zT_gNdpbr678G3TP_lwvLldv81P0CxVGfSI |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+massively+parallel+architecture+for+distributed+genetic+algorithms&rft.jtitle=Parallel+computing&rft.au=Eklund%2C+Sven+E.&rft.date=2004-05-01&rft.pub=Elsevier+B.V&rft.issn=0167-8191&rft.eissn=1872-7336&rft.volume=30&rft.issue=5&rft.spage=647&rft.epage=676&rft_id=info:doi/10.1016%2Fj.parco.2003.12.009&rft.externalDocID=S0167819104000365 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0167-8191&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0167-8191&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0167-8191&client=summon |