Populationsmodell (evolutionärer Algorithmus) (German Wikipedia)

Analysis of information sources in references of the Wikipedia article "Populationsmodell (evolutionärer Algorithmus)" in German language version.

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acm.org

dl.acm.org

  • Erick Cantú-Paz: Topologies, Migration Rates, and Multi-Population Parallel Genetic Algorithms. In: Proc. of the 1st Annual Conf. on Genetic and Evolutionary Computation (GECCO). 1999, S. 91–98 (acm.org [PDF]).
  • Gabriel Luque, Enrique Alba, Bernabé Dorronsoro: An asynchronous parallel implementation of a cellular genetic algorithm for combinatorial optimization. In: Proceedings of the 11th Annual conference on Genetic and evolutionary computation. ACM, Montreal Québec Canada 2009, ISBN 978-1-60558-325-9, S. 1395–1402, doi:10.1145/1569901.1570088 (acm.org [abgerufen am 17. Februar 2023]).

portal.acm.org

  • Vahl Scott Gordon, Keith Mathias, Darrell Whitley: Cellular Genetic Algorithms as Function Optimizers: Locality Effects. In: Conf. Proc. ACM Symposium on Applied Computing (SAC’ 94). 1994, S. 237–241, doi:10.1145/326619.326732 (acm.org).

aece.ro

  • N. Adar, G. Kuvat: Parallel Genetic Algorithms with Dynamic Topology using Cluster Computing. In: Advances in Electrical and Computer Engineering. Band 16, Nr. 3, 2016, ISSN 1582-7445, S. 73–80, doi:10.4316/AECE.2016.03011 (aece.ro [abgerufen am 16. Februar 2023]).

colostate.edu

cs.colostate.edu

  • V. Scott Gordon, Darrell Whitley: Serial and Parallel Genetic Algorithms as Function Optimizers. In: S. Forrest (Hrsg.): Conf. Proc of the Fifth International Conference on Genetic Algorithms (ICGA). Morgan Kaufmann, San Mateo, CA 1993, ISBN 978-1-55860-299-1, S. 177–183 (colostate.edu [PDF]).

doi.org

  • Yee Leung, Yong Gao, Zong-Ben Xu: Degree of population diversity – A perspective on premature convergence in genetic algorithms and its markov chain. In: IEEE Transactions on Neural Networks. Band 8, Nr. 5, 1997, ISSN 1045-9227, S. 1165–1176, doi:10.1109/72.623217 (ieee.org).
  • Erick Cantú-Paz: Efficient and Accurate Parallel Genetic Algorithms. Genetic Algorithms and Evolutionary Computation, Nr. 1. Springer US, Boston, MA 2001, ISBN 1-4613-6964-9, doi:10.1007/978-1-4615-4369-5 (englisch).
  • Martina Gorges-Schleuter: Explicit parallelism of genetic algorithms through population structures. In: Hans-Paul Schwefel, Reinhard Männer (Hrsg.): Parallel Problem Solving from Nature. Band 496. Springer-Verlag, Berlin/Heidelberg 1991, ISBN 978-3-540-54148-6, S. 150–159, doi:10.1007/bfb0029746 (springer.com [abgerufen am 31. Januar 2023]).
  • Hatem Khalloof, Mohammad Mohammad, Shadi Shahoud, Clemens Duepmeier, Veit Hagenmeyer: A Generic Flexible and Scalable Framework for Hierarchical Parallelization of Population-Based Metaheuristics. In: Conf. Proc of the 12th Int. Conf. on Management of Digital EcoSystems (MEDES’20). 2020, S. 124–131, doi:10.1145/3415958.3433041.
  • Dirk Sudholt: Parallel Evolutionary Algorithms. In: Janusz Kacprzyk, Witold Pedrycz (Hrsg.): Springer Handbook of Computational Intelligence. Springer, Berlin, Heidelberg 2015, ISBN 978-3-662-43504-5, S. 929–959, doi:10.1007/978-3-662-43505-2_46 (shef.ac.uk [PDF]).
  • K. Belkadi, M. Gourgand, M. Benyettou: Parallel genetic algorithms with migration for the hybrid flow shop scheduling problem. In: Journal of Applied Mathematics and Decision Sciences. Band 2006, 8. November 2006, ISSN 1173-9126, S. 1–17, doi:10.1155/JAMDS/2006/65746 (hindawi.com [abgerufen am 16. Februar 2023]).
  • N. Adar, G. Kuvat: Parallel Genetic Algorithms with Dynamic Topology using Cluster Computing. In: Advances in Electrical and Computer Engineering. Band 16, Nr. 3, 2016, ISSN 1582-7445, S. 73–80, doi:10.4316/AECE.2016.03011 (aece.ro [abgerufen am 16. Februar 2023]).
  • Vahl Scott Gordon, Keith Mathias, Darrell Whitley: Cellular Genetic Algorithms as Function Optimizers: Locality Effects. In: Conf. Proc. ACM Symposium on Applied Computing (SAC’ 94). 1994, S. 237–241, doi:10.1145/326619.326732 (acm.org).
  • Dudy Lim, Yew-Soon Ong, Yaochu Jin, Bernhard Sendhoff, Bu-Sung Lee: Efficient Hierarchical Parallel Genetic Algorithms using Grid computing. In: Future Generation Computer Systems. Band 23, Nr. 4, Mai 2007, S. 658–670, doi:10.1016/j.future.2006.10.008 (researchgate.net [abgerufen am 17. Februar 2023]).
  • M. Giacobini, M. Tomassini, A.G.B. Tettamanzi, E. Alba: Selection Intensity in Cellular Evolutionary Algorithms for Regular Lattices. In: IEEE Transactions on Evolutionary Computation. Band 9, Nr. 5, Oktober 2005, ISSN 1089-778X, S. 489–505, doi:10.1109/TEVC.2005.850298 (researchgate.net [abgerufen am 16. Februar 2023]).
  • Enrique Alba, José Ma Troya: Cellular Evolutionary Algorithms: Evaluating the Influence of Ratio. In: Parallel Problem Solving from Nature PPSN VI. Band 1917. Springer, Berlin, Heidelberg 2000, ISBN 978-3-540-41056-0, S. 29–38, doi:10.1007/3-540-45356-3_3 (springer.com [abgerufen am 16. Februar 2023]).
  • Enrique Alba, Bernabè Dorronsoro: Introduction to Cellular Genetic Algorithms. In: Cellular Genetic Algorithms. Band 42. Springer US, Boston, MA 2008, ISBN 978-0-387-77609-5, S. 3–20, doi:10.1007/978-0-387-77610-1_1 (springer.com [abgerufen am 31. Januar 2023]).
  • Jayshree Sarma, Kenneth De Jong: An analysis of the effects of neighborhood size and shape on local selection algorithms. In: Parallel Problem Solving from Nature — PPSN IV. Band 1141. Springer, Berlin, Heidelberg 1996, ISBN 978-3-540-61723-5, S. 236–244, doi:10.1007/3-540-61723-x_988 (researchgate.net [abgerufen am 17. Februar 2023]).
  • Martina Gorges-Schleuter: A comparative study of global and local selection in evolution strategies. In: Parallel Problem Solving from Nature — PPSN V. Band 1498. Springer Berlin Heidelberg, Berlin, Heidelberg 1998, ISBN 3-540-65078-4, S. 367–377, doi:10.1007/bfb0056879.
  • Martina Gorges-Schleuter, Ingo Sieber, Wilfried Jakob: Local Interaction Evolution Strategies for Design Optimization. In: Conf. Proc. Congress on Evolutionary Computation (CEC 99). IEEE press, Piscataway, N.J., USA 1999, S. 2167–2174, doi:10.1109/CEC.1999.785544 (ieee.org [PDF]).
  • Christian Blume, Wilfried Jakob: GLEAM - General Learning Evolutionary Algorithm and Method: Ein Evolutionärer Algorithmus und seine Anwendungen. In: Schriftenreihe des Instituts für Angewandte Informatik – Automatisierungstechnik. Band, Nr. 32. KIT Scientific Publishing, Karlsruhe 2009, ISBN 978-3-86644-436-2, doi:10.5445/KSP/1000013553.
  • Wen-Yang Lin, Tzung-Pei Hong, Shu-Min Liu: On adapting migration parameters for multi-population genetic algorithms. In: 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583). Band 6. IEEE, The Hague, Netherlands 2004, ISBN 978-0-7803-8567-2, S. 5731–5735, doi:10.1109/ICSMC.2004.1401108 (ieee.org [abgerufen am 16. Februar 2023]).
  • Tzung-Pei Hong, Wen-Yang Lin, Shu-Min Liu, Jiann-Horng Lin: Dynamically Adjusting Migration Rates for Multi-Population Genetic Algorithms. In: Journal of Advanced Computational Intelligence and Intelligent Informatics. Band 11, Nr. 4, 20. April 2007, ISSN 1883-8014, S. 410–415, doi:10.20965/jaciii.2007.p0410 (fujipress.jp [abgerufen am 16. Februar 2023]).
  • Gabriel Luque, Enrique Alba: Parallel Genetic Algorithms (= Studies in Computational Intelligence. Nr. 367). Springer, Berlin, Heidelberg 2011, ISBN 978-3-642-22083-8, doi:10.1007/978-3-642-22084-5 (springer.com [abgerufen am 17. Februar 2023]).
  • Gabriel Luque, Enrique Alba, Bernabé Dorronsoro: An asynchronous parallel implementation of a cellular genetic algorithm for combinatorial optimization. In: Proceedings of the 11th Annual conference on Genetic and evolutionary computation. ACM, Montreal Québec Canada 2009, ISBN 978-1-60558-325-9, S. 1395–1402, doi:10.1145/1569901.1570088 (acm.org [abgerufen am 17. Februar 2023]).
  • Zhongwen Luo, Hongzhi Liu: Cellular Genetic Algorithms and Local Search for 3-SAT problem on Graphic Hardware. In: 2006 IEEE International Conference on Evolutionary Computation. IEEE, Vancouver, BC, Canada 2006, ISBN 978-0-7803-9487-2, S. 2988–2992, doi:10.1109/CEC.2006.1688685 (ieee.org [abgerufen am 17. Februar 2023]).
  • S. Cahon, N. Melab, E.-G. Talbi: ParadisEO: A Framework for the Reusable Design of Parallel and Distributed Metaheuristics. In: Journal of Heuristics. Band 10, Nr. 3, Mai 2004, ISSN 1381-1231, S. 357–380, doi:10.1023/B:HEUR.0000026900.92269.ec (springer.com [abgerufen am 17. Februar 2023]).
  • Raúl García-Calvo, Jl Guisado, Fernando Diaz-del-Rio, Antonio Córdoba, Francisco Jiménez-Morales: Graphics Processing Unit–Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks. In: Evolutionary Bioinformatics. Band 14, Januar 2018, ISSN 1176-9343, S. 117693431876788, doi:10.1177/1176934318767889, PMID 29662297 (sagepub.com [abgerufen am 17. Februar 2023]).

emis.de

subs.emis.de

  • Paul Jähne: Overview of the current state of research on parallelisation of evolutionary algorithms on graphic cards. In: H.C. Mayr, M. Pinzger (Hrsg.): Informatik 2016 Tagung vom 26. - 30. September 2016. Gesellschaft für Informatik, Bonn 2016, ISBN 978-3-88579-653-4 (emis.de [PDF; abgerufen am 17. Februar 2023]).

fujipress.jp

  • Tzung-Pei Hong, Wen-Yang Lin, Shu-Min Liu, Jiann-Horng Lin: Dynamically Adjusting Migration Rates for Multi-Population Genetic Algorithms. In: Journal of Advanced Computational Intelligence and Intelligent Informatics. Band 11, Nr. 4, 20. April 2007, ISSN 1883-8014, S. 410–415, doi:10.20965/jaciii.2007.p0410 (fujipress.jp [abgerufen am 16. Februar 2023]).

hindawi.com

  • K. Belkadi, M. Gourgand, M. Benyettou: Parallel genetic algorithms with migration for the hybrid flow shop scheduling problem. In: Journal of Applied Mathematics and Decision Sciences. Band 2006, 8. November 2006, ISSN 1173-9126, S. 1–17, doi:10.1155/JAMDS/2006/65746 (hindawi.com [abgerufen am 16. Februar 2023]).

ieee.org

ieeexplore.ieee.org

  • Yee Leung, Yong Gao, Zong-Ben Xu: Degree of population diversity – A perspective on premature convergence in genetic algorithms and its markov chain. In: IEEE Transactions on Neural Networks. Band 8, Nr. 5, 1997, ISSN 1045-9227, S. 1165–1176, doi:10.1109/72.623217 (ieee.org).
  • Martina Gorges-Schleuter, Ingo Sieber, Wilfried Jakob: Local Interaction Evolution Strategies for Design Optimization. In: Conf. Proc. Congress on Evolutionary Computation (CEC 99). IEEE press, Piscataway, N.J., USA 1999, S. 2167–2174, doi:10.1109/CEC.1999.785544 (ieee.org [PDF]).
  • Wen-Yang Lin, Tzung-Pei Hong, Shu-Min Liu: On adapting migration parameters for multi-population genetic algorithms. In: 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583). Band 6. IEEE, The Hague, Netherlands 2004, ISBN 978-0-7803-8567-2, S. 5731–5735, doi:10.1109/ICSMC.2004.1401108 (ieee.org [abgerufen am 16. Februar 2023]).
  • Zhongwen Luo, Hongzhi Liu: Cellular Genetic Algorithms and Local Search for 3-SAT problem on Graphic Hardware. In: 2006 IEEE International Conference on Evolutionary Computation. IEEE, Vancouver, BC, Canada 2006, ISBN 978-0-7803-9487-2, S. 2988–2992, doi:10.1109/CEC.2006.1688685 (ieee.org [abgerufen am 17. Februar 2023]).

nih.gov

ncbi.nlm.nih.gov

  • Raúl García-Calvo, Jl Guisado, Fernando Diaz-del-Rio, Antonio Córdoba, Francisco Jiménez-Morales: Graphics Processing Unit–Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks. In: Evolutionary Bioinformatics. Band 14, Januar 2018, ISSN 1176-9343, S. 117693431876788, doi:10.1177/1176934318767889, PMID 29662297 (sagepub.com [abgerufen am 17. Februar 2023]).

researchgate.net

  • Dudy Lim, Yew-Soon Ong, Yaochu Jin, Bernhard Sendhoff, Bu-Sung Lee: Efficient Hierarchical Parallel Genetic Algorithms using Grid computing. In: Future Generation Computer Systems. Band 23, Nr. 4, Mai 2007, S. 658–670, doi:10.1016/j.future.2006.10.008 (researchgate.net [abgerufen am 17. Februar 2023]).
  • M. Giacobini, M. Tomassini, A.G.B. Tettamanzi, E. Alba: Selection Intensity in Cellular Evolutionary Algorithms for Regular Lattices. In: IEEE Transactions on Evolutionary Computation. Band 9, Nr. 5, Oktober 2005, ISSN 1089-778X, S. 489–505, doi:10.1109/TEVC.2005.850298 (researchgate.net [abgerufen am 16. Februar 2023]).
  • Jayshree Sarma, Kenneth De Jong: An analysis of the effects of neighborhood size and shape on local selection algorithms. In: Parallel Problem Solving from Nature — PPSN IV. Band 1141. Springer, Berlin, Heidelberg 1996, ISBN 978-3-540-61723-5, S. 236–244, doi:10.1007/3-540-61723-x_988 (researchgate.net [abgerufen am 17. Februar 2023]).
  • Wilfried Jakob, Martina Gorges-Schleuter, Christian Blume: Application of Genetic Algorithms to Task Planning and Learning. In: Reinhard Männer, Bernard Manderick (Hrsg.): Parallel Problem Solving from Nature 2, PPSN-II. North Holland, Amsterdam 1992, S. 291–300, S. 292 (researchgate.net).

sagepub.com

journals.sagepub.com

  • Raúl García-Calvo, Jl Guisado, Fernando Diaz-del-Rio, Antonio Córdoba, Francisco Jiménez-Morales: Graphics Processing Unit–Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks. In: Evolutionary Bioinformatics. Band 14, Januar 2018, ISSN 1176-9343, S. 117693431876788, doi:10.1177/1176934318767889, PMID 29662297 (sagepub.com [abgerufen am 17. Februar 2023]).

shef.ac.uk

staffdcs.shef.ac.uk

springer.com

link.springer.com

springer.com

tu-dortmund.de

ls11-cs.tu-dortmund.de

  • Joachim Sprave: Linear Neighborhood Evolution Strategy. In: Conf. Proc. of the 3rd Annual Conference on Evolutionary Programming. World Scientific, Singapore 1994, S. 42–51 (tu-dortmund.de [PDF]).

uma.es

neo.lcc.uma.es

  • Erick Cantú-Paz: A survey of parallel genetic algorithms. In: Calculateurs Paralleles. Band 10, Nr. 2, 1998, S. 141–171 (uma.es [PDF; abgerufen am 20. Juli 2022]).

unlp.edu.ar

journal.info.unlp.edu.ar

  • Enrique Alba Torres, Bernabé Dorronsoro, Hugo Alfonso: Cellular Memetic Algorithms. In: Journal of Computer Science and Technology. Band 5, Nr. 4, 2005, S. 257–263 (edu.ar).

zdb-katalog.de

  • Yee Leung, Yong Gao, Zong-Ben Xu: Degree of population diversity – A perspective on premature convergence in genetic algorithms and its markov chain. In: IEEE Transactions on Neural Networks. Band 8, Nr. 5, 1997, ISSN 1045-9227, S. 1165–1176, doi:10.1109/72.623217 (ieee.org).
  • K. Belkadi, M. Gourgand, M. Benyettou: Parallel genetic algorithms with migration for the hybrid flow shop scheduling problem. In: Journal of Applied Mathematics and Decision Sciences. Band 2006, 8. November 2006, ISSN 1173-9126, S. 1–17, doi:10.1155/JAMDS/2006/65746 (hindawi.com [abgerufen am 16. Februar 2023]).
  • N. Adar, G. Kuvat: Parallel Genetic Algorithms with Dynamic Topology using Cluster Computing. In: Advances in Electrical and Computer Engineering. Band 16, Nr. 3, 2016, ISSN 1582-7445, S. 73–80, doi:10.4316/AECE.2016.03011 (aece.ro [abgerufen am 16. Februar 2023]).
  • M. Giacobini, M. Tomassini, A.G.B. Tettamanzi, E. Alba: Selection Intensity in Cellular Evolutionary Algorithms for Regular Lattices. In: IEEE Transactions on Evolutionary Computation. Band 9, Nr. 5, Oktober 2005, ISSN 1089-778X, S. 489–505, doi:10.1109/TEVC.2005.850298 (researchgate.net [abgerufen am 16. Februar 2023]).
  • Tzung-Pei Hong, Wen-Yang Lin, Shu-Min Liu, Jiann-Horng Lin: Dynamically Adjusting Migration Rates for Multi-Population Genetic Algorithms. In: Journal of Advanced Computational Intelligence and Intelligent Informatics. Band 11, Nr. 4, 20. April 2007, ISSN 1883-8014, S. 410–415, doi:10.20965/jaciii.2007.p0410 (fujipress.jp [abgerufen am 16. Februar 2023]).
  • S. Cahon, N. Melab, E.-G. Talbi: ParadisEO: A Framework for the Reusable Design of Parallel and Distributed Metaheuristics. In: Journal of Heuristics. Band 10, Nr. 3, Mai 2004, ISSN 1381-1231, S. 357–380, doi:10.1023/B:HEUR.0000026900.92269.ec (springer.com [abgerufen am 17. Februar 2023]).
  • Raúl García-Calvo, Jl Guisado, Fernando Diaz-del-Rio, Antonio Córdoba, Francisco Jiménez-Morales: Graphics Processing Unit–Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks. In: Evolutionary Bioinformatics. Band 14, Januar 2018, ISSN 1176-9343, S. 117693431876788, doi:10.1177/1176934318767889, PMID 29662297 (sagepub.com [abgerufen am 17. Februar 2023]).