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Evolutionärer Algorithmus (German Wikipedia)

Analysis of information sources in references of the Wikipedia article "Evolutionärer Algorithmus" in German language version.

Last modified:

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8861doi.org
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1910zdb-katalog.de
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128researchgate.net
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64elsevier.com
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32springer.com
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31arxiv.org
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acm.org (Global: 1,200th place; German: 2,201st place)

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arxiv.org (Global: 49th place; German: 152nd place)

bl.uk (Global: 1,181st place; German: 2,839th place)

ethos.bl.uk

  • Natalio Krasnogor: Studies on the Theory and Design Space of Memetic Algorithms. Dissertation. University of the West of England, Bristol, UK 2002, S. 23 (englisch, bl.uk).

darwin-online.org.uk (Global: 3,419th place; German: 7,088th place)

  • Charles Darwin: The Origin of Species by Means of Natural Selection. 6. Auflage. John Murray, London 1872 (englisch, org.uk).

dinmedia.de (Global: low place; German: 3,482nd place)

  • VDI/VDE (Hrsg.): VDI/VDE 3550 Blatt 3:2003-02. Weißdruck. DIN Media, Berlin 2003 (18 S., dinmedia.de).

doi.org (Global: 2nd place; German: 3rd place)

  • J.D. Lohn, D.S. Linden, G.S. Hornby, W.F. Kraus: Evolutionary design of an X-band antenna for NASA's Space Technology 5 mission. In: Antennas and Propagation Society International Symposium. IEEE, 2004, ISBN 978-0-7803-8302-9, S. 2313–2316 Vol.3, doi:10.1109/APS.2004.1331834.
  • Applications of Evolutionary Computation: EvoApplications 2012: Proceedings (= Lecture Notes in Computer Science. Band 7248). Springer, Berlin, Heidelberg 2012, ISBN 978-3-642-29177-7, doi:10.1007/978-3-642-29178-4.
  • Keshav P. Dahal, Kay Chen Tan, Peter I. Cowling (Hrsg.): Evolutionary Scheduling. SCI, Nr. 49. Springer, Berlin, Heidelberg 2007, ISBN 978-3-540-48582-7, doi:10.1007/978-3-540-48584-1.
  • Ian C. Parmee: Strategies for the Integration of Evolutionary/Adaptive Search with the Engineering Design Process. In: Dipankar Dasgupta, Zbigniew Michalewicz (Hrsg.): Evolutionary Algorithms in Engineering Applications. Springer Berlin Heidelberg, Berlin, Heidelberg 1997, ISBN 3-642-08282-3, S. 453–477, doi:10.1007/978-3-662-03423-1_25.
  • Christian Blume: Optimized Collision Free Robot Move Statement Generation by the Evolutionary Software GLEAM. In: S. Cagnoni (Hrsg.): Real-World Applications of Evolutionary Computing. LNCS 1803. Springer, Berlin, Heidelberg 2000, ISBN 3-540-67353-9, S. 330–341, doi:10.1007/3-540-45561-2_32.
  • Karsten Weicker: Evolutionäre Algorithmen. Springer Fachmedien Wiesbaden, Wiesbaden 2015, ISBN 978-3-658-09957-2, doi:10.1007/978-3-658-09958-9.
    1. ↑ S. 25
    2. Volker Nissen: Evolutionäre Algorithmen. Deutscher Universitätsverlag, Wiesbaden 1994, ISBN 3-8244-0217-3, doi:10.1007/978-3-322-83430-0.
      1. ↑ Abb. 3.4, S. 27
      2. A.E. Eiben, J.E. Smith: Introduction to Evolutionary Computing (= Natural Computing Series). 2. Auflage. Springer, Berlin, Heidelberg 2015, ISBN 978-3-662-44873-1, doi:10.1007/978-3-662-44874-8.
        1. ↑ What Is an Evolutionary Algorithm?, S. 25–28 und Fig. 3.1, S. 26,
        2. Heikki Maaranen, Kaisa Miettinen, Antti Penttinen: On initial populations of a genetic algorithm for continuous optimization problems. In: Journal of Global Optimization. Band 37, Nr. 3, 23. Januar 2007, ISSN 0925-5001, S. 405–436, doi:10.1007/s10898-006-9056-6 (researchgate.net [abgerufen am 1. Oktober 2023]).
        3. Borhan Kazimipour, Xiaodong Li, A. K. Qin: A review of population initialization techniques for evolutionary algorithms. IEEE, 2014, ISBN 978-1-4799-1488-3, S. 2585–2592, doi:10.1109/CEC.2014.6900618 (ieee.org [abgerufen am 1. Oktober 2023]).
        4. Wilfried Jakob: HyGLEAM–An Approach to Generally Applicable Hybridization of Evolutionary Algorithms. In: Parallel Problem Solving from Nature — PPSN VII. Band 2439. Springer, Berlin, Heidelberg 2002, ISBN 3-540-44139-5, S. 527–536, doi:10.1007/3-540-45712-7_51 (researchgate.net [abgerufen am 1. Oktober 2023]).
        5. Muhanad Tahrir Younis, Shengxiang Yang, Benjamin Passow: Meta-Heuristically Seeded Genetic Algorithm for Independent Job Scheduling in Grid Computing. In: Applications of Evolutionary Computation. Band 10199. Springer International Publishing, Cham 2017, ISBN 978-3-319-55848-6, S. 177–189, doi:10.1007/978-3-319-55849-3_12.
        6. Tobias Friedrich, Markus Wagner: Seeding the initial population of multi-objective evolutionary algorithms: A computational study. In: Applied Soft Computing. Band 33, August 2015, S. 223–230, doi:10.1016/j.asoc.2015.04.043 (elsevier.com [abgerufen am 1. Oktober 2023]).
        7. Musrrat Ali, Millie Pant, Ajith Abraham: Unconventional initialization methods for differential evolution. In: Applied Mathematics and Computation. Band 219, Nr. 9, Januar 2013, S. 4474–4494, doi:10.1016/j.amc.2012.10.053 (elsevier.com [abgerufen am 1. Oktober 2023]).
        8. Borhan Kazimipour, Xiaodong Li, A. K. Qin: Initialization methods for large scale global optimization. In: IEEE Congress on Evolutionary Computation. 2013, S. 2750–2757, doi:10.1109/CEC.2013.6557902 (ieee.org).
        9. Thomas Bäck, Hans-Paul Schwefel: An Overview of Evolutionary Algorithms for Parameter Optimization. In: Evolutionary Computation. Band 1, Nr. 1, 1. März 1993, ISSN 1063-6560, S. 1–23, doi:10.1162/evco.1993.1.1.1.
          1. ↑ S. 5
          2. Christian Blume, Wilfried Jakob: GLEAM: General Learning Evolutionary Algorithm and Method ; ein evolutionärer Algorithmus und seine Anwendungen (= Schriftenreihe des Instituts für Angewandte Informatik. Nr. 32). KIT Scientific Publishing, Karlsruhe 2009, ISBN 978-3-86644-436-2, doi:10.5445/KSP/1000013553.
            1. ↑ S. 14
            2. Mitsuo Gen, Runwei Cheng: Genetic Algorithms and Engineering Optimization. Wiley, New York, 2000, S. 8. ISBN 978-0-471-31531-5. doi:10.1002/9780470172261
            3. William M. Spears: The Role of Mutation and Recombination in Evolutionary Algorithms. Springer, Berlin, Heidelberg, 2000, S. 225f. doi:10.1007/978-3-662-04199-4
            4. Bill Worzel, Terence Soule, Rick Riolo: Genetic Programming Theory and Practice VI. Springer, Berlin, Heidelberg, 2009, S. 62. doi:10.1007/978-0-387-87623-8
            5. Oscar Cordón, Francisco Herrera, Frank Hoffmann, Luis Magdalena: Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases. World Scientific Publishing, Singapore, 2002, S. 95. doi:10.1142/4177
            6. Ralf Mikut, Frank Hendrich: Produktionsreihenfolgeplanung in Ringwalzwerken mit wissensbasierten und evolutionären Methoden. In: Automatisierungstechnik. Band 46, Nr. 1, Januar 1998, ISSN 2196-677X, S. 15–21, doi:10.1524/auto.1998.46.1.15.
            7. Ferrante Neri, Carlos Cotta, Pablo Moscato (Eds.): Handbook of Memetic Algorithms (= Studies in Computational Intelligence. Nr. 379). Springer, Berlin, Heidelberg 2012, ISBN 978-3-642-26942-4, doi:10.1007/978-3-642-23247-3.
            8. 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.
            9. Bernabe Dorronsoro, Enrique Alba: Cellular Genetic Algorithms (= Operations Research/Computer Science Interfaces Series. Band 42). Springer US, Boston, MA 2008, ISBN 978-0-387-77609-5, doi:10.1007/978-0-387-77610-1.
            10. Darrell Whitley: A Genetic Algorithm Tutorial. In: Statistics and Computing. Band 4, Nr. 2, Juni 1994, ISSN 0960-3174, Criticism of the schema theorem, S. 77, doi:10.1007/BF00175354.
            11. Volker Nissen: Einführung in evolutionäre Algorithmen: Optimierung nach dem Vorbild der Evolution. Vieweg, Braunschweig 1997, ISBN 3-528-05499-9, Das Schema-Theorem und seine Kritiker, S. 85–92, doi:10.1007/978-3-322-93861-9.
            12. Zbigniew Michalewicz: Genetic Algorithms + Data Structures = Evolution Programs. Dritte, überarbeitete und erweiterte Auflage. Springer, Berlin, Heidelberg 1996, ISBN 3-662-03315-1, doi:10.1007/978-3-662-03315-9.
            13. Hitoshi Iba, Nasimul Noman: New Frontier in Evolutionary Algorithms: Theory and Applications. IMPERIAL COLLEGE PRESS, 2011, ISBN 978-1-84816-681-3, doi:10.1142/p769.
            14. Ernesto Sanchez, Giovanni Squillero, Alberto Tonda: Industrial Applications of Evolutionary Algorithms (= Intelligent Systems Reference Library. Band 34). Springer, Berlin, Heidelberg 2012, ISBN 978-3-642-27466-4, doi:10.1007/978-3-642-27467-1.
            15. Dipankar Dasgupta, Zbigniew Michalewicz (Hrsg.): Evolutionary Algorithms in Engineering Applications. Springer, Berlin, Heidelberg 1997, ISBN 3-642-08282-3, doi:10.1007/978-3-662-03423-1.
            16. Adam Slowik, Halina Kwasnicka: Evolutionary algorithms and their applications to engineering problems. In: Neural Computing and Applications. Band 32, Nr. 16, August 2020, ISSN 0941-0643, S. 12363–12379, doi:10.1007/s00521-020-04832-8.
            17. Nantiwat Pholdee, Sujin Bureerat: Multiobjective Trajectory Planning of a 6D Robot based on Multiobjective Meta Heuristic Search. ACM, 2018, ISBN 978-1-4503-6553-6, S. 352–356, doi:10.1145/3301326.3301356 (acm.org [abgerufen am 15. September 2024]).
            18. David G. Mayer: Evolutionary Algorithms and Agricultural Systems. Springer US, Boston, MA 2002, ISBN 1-4613-5693-8, doi:10.1007/978-1-4615-1717-7.
            19. Gary Fogel, David Corne: Evolutionary Computation in Bioinformatics. Elsevier, 2003, ISBN 1-55860-797-8, doi:10.1016/b978-1-55860-797-2.x5000-8.
            20. Wilfried Jakob: Applying Evolutionary Algorithms Successfully - A Guide Gained from Real-world Applications. KIT Scientific Working Papers, Nr. 170. KIT Scientific Publishing, 2021, ISSN 2194-1629, doi:10.5445/IR/1000135763, arxiv:2107.11300 (englisch, kit.edu).
            21. Hartmut Pohlheim: Evolutionäre Algorithmen - Verfahren, Operatoren und Hinweise für die Praxis. VDI-Buch. Springer, Berlin, Heidelberg 2000, ISBN 3-642-63052-9, doi:10.1007/978-3-642-57137-4.
            22. Shu-Heng Chen: Evolutionary Computation in Economics and Finance. Physica, Heidelberg, 2002. S. 6. doi:10.1007/978-3-7908-1784-3
            23. Claus Aranha, Hitoshi Iba: Application of a Memetic Algorithm to the Portfolio Optimization Problem. In: Wayne Wobcke, Mengjie Zhang (Hrsg.): Advances in Artificial Intelligence. AI 2008. LNCS 5360. Springer, Berlin, Heidelberg, 2008. doi:10.1007/978-3-540-89378-3_52
            24. Kalyanmoy Deb: GeneAS: A Robust Optimal Design Technique for Mechanical Component Design. In: Dipankar Dasgupta, Zbigniew Michalewicz (Hrsg.): Evolutionary Algorithms in Engineering Applications. Springer, Berlin, Heidelberg 1997, ISBN 3-642-08282-3, S. 497–514, doi:10.1007/978-3-662-03423-1_27.
            25. Mark P. Kleeman, Gary B. Lamont: Scheduling of Flow-Shop, Job-Shop, and Combined Scheduling Problems using MOEAs with Fixed and Variable Length Chromosomes. In: Keshav P. Dahal, Kay Chen Tan, Peter I. Cowling (Hrsg.): Evolutionary Scheduling (= Studies in Computational Intelligence. Band 49). Springer, Berlin, Heidelberg 2007, ISBN 978-3-540-48582-7, S. 49–99, doi:10.1007/978-3-540-48584-1.
            26. Kazi Shah Nawaz Ripon, Chi-Ho Tsang, Sam Kwong: An Evolutionary Approach for Solving the Multi-Objective Job-Shop Scheduling Problem. In: Keshav P. Dahal, Kay Chen Tan, Peter I. Cowling (Hrsg.): Evolutionary Scheduling (= Studies in Computational Intelligence. Band 49). Springer, Berlin, Heidelberg 2007, ISBN 978-3-540-48582-7, S. 165–195, doi:10.1007/978-3-540-48584-1.
            27. Marek Mika, Grzegorz Waligóra, Jan Węglarz: Modelling and solving grid resource allocation problem with network resources for workflow applications. In: Journal of Scheduling. Band 14, Nr. 3, Juni 2011, ISSN 1094-6136, S. 291–306, doi:10.1007/s10951-009-0158-0 (springer.com [abgerufen am 15. September 2024]).
            28. Wilfried Jakob, Sylvia Strack, Alexander Quinte, Günther Bengel, Karl-Uwe Stucky: Fast Rescheduling of Multiple Workflows to Constrained Heterogeneous Resources Using Multi-Criteria Memetic Computing. In: Algorithms. Band 6, Nr. 2, 22. April 2013, ISSN 1999-4893, S. 245–277, doi:10.3390/a6020245 (mdpi.com [abgerufen am 8. Februar 2022]).
            29. Alberto Colorni, Marco Dorigo, Vittorio Maniezzo: Genetic Algorithms: A New Approach to the Timetable Problem. In: M. Akgül, H.W. Hamacher, S. Tüfekçi (Hrsg.): Combinatorial Optimization. NATO ASI Series (Series F: Computer and Systems Sciences), Nr. 82. Springer, Berlin, Heidelberg 1992, ISBN 3-642-77491-1, S. 235–239, doi:10.1007/978-3-642-77489-8_14.
            30. B. Paechter, A. Cumming, H. Luchian: The use of local search suggestion lists for improving the solution of timetable problems with evolutionary algorithms. In: Terence C. Fogarty (Hrsg.): Evolutionary computing: AISB Workshop, Brighton, U.K.: selected papers. Springer, Berlin, Heidelberg 1996, ISBN 3-540-61749-3, doi:10.1007/3-540-60469-3_27.
            31. Dipankar Dasgupta: Optimal Scheduling of Thermal Power Generation Using Evolutionary Algorithms. In: Dipankar Dasgupta, Zbigniew Michalewicz (Hrsg.): Evolutionary Algorithms in Engineering Applications. Springer, Berlin, Heidelberg 1997, ISBN 3-642-08282-3, S. 317–328, doi:10.1007/978-3-662-03423-1_18.
            32. Robert Axelrod: Die Evolution der Kooperation. Oldenbourg, München 1987; 7. Auflage, 2014. ISBN 978-3-486-59172-9. doi:10.1524/9783486851748
            33. W. Leo Meerts, Michael Schmitt: Application of genetic algorithms in automated assignments of high-resolution spectra. In: International Reviews in Physical Chemistry. Band 25, Nr. 3, 1. Juli 2006, ISSN 0144-235X, S. 353–406, doi:10.1080/01442350600785490.
            34. Ingo Rechenberg: Evolutionsstrategie – Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Dissertation. Frommann-Holzboog, 1973, ISBN 3-7728-0373-3, doi:10.1002/fedr.19750860506.
            35. Hans-Paul Schwefel: Evolutionsstrategie und numerische Optimierung. Dissertation. Technische Universität, Berlin 1975, doi:10.1007/978-3-0348-5927-1_5 (researchgate.net).
            36. Darrell Whitley: An overview of evolutionary algorithms: practical issues and common pitfalls. In: Information and Software Technology. Band 43, Nr. 14, Dezember 2001, S. 817–831, doi:10.1016/S0950-5849(01)00188-4 (elsevier.com [abgerufen am 8. Februar 2022]).
            37. Lukáš Sekanina: Evolvable Components: From Theory to Hardware Implementations. Springer, Berlin, Heidelberg, 2004, S. 27. doi:10.1007/978-3-642-18609-7
            38. Chuan-Kang Ting: On the Mean Convergence Time of Multi-parent Genetic Algorithms Without Selection. In: Advances in Artificial Life. Springer, Berlin, Heidelberg 2005, ISBN 978-3-540-31816-3, S. 403–412, doi:10.1007/11553090_41.
            39. Nikolaus Hansen, Andreas Ostermeier: Completely Derandomized Self-Adaptation in Evolution Strategies. In: Evolutionary Computation. Band 9, Nr. 2, Juni 2001, ISSN 1063-6560, S. 159–195, doi:10.1162/106365601750190398.
            40. Nikolaus Hansen, Stefan Kern: Evaluating the CMA Evolution Strategy on Multimodal Test Functions. In: Conf. Proc. of Parallel Problem Solving from Nature - PPSN VIII. LNCS, Nr. 3242. Springer Berlin Heidelberg, Berlin, Heidelberg 2004, ISBN 3-540-23092-0, S. 282–291, doi:10.1007/978-3-540-30217-9_29.
            41. Daniel Mora-Melià, F. Javier Martínez-Solano, Pedro L. Iglesias-Rey, Jimmy H. Gutiérrez-Bahamondes: Population Size Influence on the Efficiency of Evolutionary Algorithms to Design Water Networks. In: Procedia Engineering. Band 186, 2017, S. 341–348, doi:10.1016/j.proeng.2017.03.209 (elsevier.com [abgerufen am 29. November 2025]).
            42. Dana Vrajitoru: Large Population or Many Generations for Genetic Algorithms? Implications in Information Retrieval. In: Soft Computing in Information Retrieval. Band 50. Physica-Verlag HD, Heidelberg 2000, ISBN 978-3-7908-2473-5, S. 199–222, doi:10.1007/978-3-7908-1849-9_9.
            43. Martin Briesch, Dominik Sobania, Franz Rothlauf: On the Trade-Off between Population Size and Number of Generations in GP for Program Synthesis. ACM, 2023, ISBN 979-84-0070120-7, S. 535–538, doi:10.1145/3583133.3590681 (acm.org [abgerufen am 29. November 2025]).
            44. Julian F. Miller: Cartesian Genetic Programming. Natural Computing Series. Springer, Berlin, Heidelberg, 2011, S. 63. doi:10.1007/978-3-642-17310-3_2
            45. Thomas Bäck, David B. Fogel, Zbigniew Michalewicz (Hrsg.): Evolutionary Computation 1. Institute of Physics Publishing, Bristol; Philadelphia 2000, ISBN 978-0-7503-0664-5, Glossary, S. xxx und S. xxxvii, doi:10.1201/9781482268713 (worldcat.org [abgerufen am 16. September 2024]).

elsevier.com (Global: 365th place; German: 435th place)

linkinghub.elsevier.com

  • Tobias Friedrich, Markus Wagner: Seeding the initial population of multi-objective evolutionary algorithms: A computational study. In: Applied Soft Computing. Band 33, August 2015, S. 223–230, doi:10.1016/j.asoc.2015.04.043 (elsevier.com [abgerufen am 1. Oktober 2023]).
  • Musrrat Ali, Millie Pant, Ajith Abraham: Unconventional initialization methods for differential evolution. In: Applied Mathematics and Computation. Band 219, Nr. 9, Januar 2013, S. 4474–4494, doi:10.1016/j.amc.2012.10.053 (elsevier.com [abgerufen am 1. Oktober 2023]).
  • Darrell Whitley: An overview of evolutionary algorithms: practical issues and common pitfalls. In: Information and Software Technology. Band 43, Nr. 14, Dezember 2001, S. 817–831, doi:10.1016/S0950-5849(01)00188-4 (elsevier.com [abgerufen am 8. Februar 2022]).
  • Daniel Mora-Melià, F. Javier Martínez-Solano, Pedro L. Iglesias-Rey, Jimmy H. Gutiérrez-Bahamondes: Population Size Influence on the Efficiency of Evolutionary Algorithms to Design Water Networks. In: Procedia Engineering. Band 186, 2017, S. 341–348, doi:10.1016/j.proeng.2017.03.209 (elsevier.com [abgerufen am 29. November 2025]).

evostar.org (Global: low place; German: low place)

ieee.org (Global: 912th place; German: 894th place)

ieeexplore.ieee.org

  • Borhan Kazimipour, Xiaodong Li, A. K. Qin: A review of population initialization techniques for evolutionary algorithms. IEEE, 2014, ISBN 978-1-4799-1488-3, S. 2585–2592, doi:10.1109/CEC.2014.6900618 (ieee.org [abgerufen am 1. Oktober 2023]).
  • Borhan Kazimipour, Xiaodong Li, A. K. Qin: Initialization methods for large scale global optimization. In: IEEE Congress on Evolutionary Computation. 2013, S. 2750–2757, doi:10.1109/CEC.2013.6557902 (ieee.org).

kit.edu (Global: 520th place; German: 858th place)

publikationen.bibliothek.kit.edu

mdpi.com (Global: 1,887th place; German: 1,072nd place)

  • Wilfried Jakob, Sylvia Strack, Alexander Quinte, Günther Bengel, Karl-Uwe Stucky: Fast Rescheduling of Multiple Workflows to Constrained Heterogeneous Resources Using Multi-Criteria Memetic Computing. In: Algorithms. Band 6, Nr. 2, 22. April 2013, ISSN 1999-4893, S. 245–277, doi:10.3390/a6020245 (mdpi.com [abgerufen am 8. Februar 2022]).

researchgate.net (Global: 96th place; German: 105th place)

  • Heikki Maaranen, Kaisa Miettinen, Antti Penttinen: On initial populations of a genetic algorithm for continuous optimization problems. In: Journal of Global Optimization. Band 37, Nr. 3, 23. Januar 2007, ISSN 0925-5001, S. 405–436, doi:10.1007/s10898-006-9056-6 (researchgate.net [abgerufen am 1. Oktober 2023]).
  • Wilfried Jakob: HyGLEAM–An Approach to Generally Applicable Hybridization of Evolutionary Algorithms. In: Parallel Problem Solving from Nature — PPSN VII. Band 2439. Springer, Berlin, Heidelberg 2002, ISBN 3-540-44139-5, S. 527–536, doi:10.1007/3-540-45712-7_51 (researchgate.net [abgerufen am 1. Oktober 2023]).
  • Darrell Whitley: The GENITOR Algorithm and Selective Pressure: Why Rank-Based Allocation of Reproductive Trials is Best. In: J. David Schaffer (Hrsg.): Conf. Proc. of the 3rd Int. Conf. on Genetic Algorithms and Their Applications (ICGA). Morgan Kaufmann Publishers, San Francisco, CA 1989, ISBN 1-55860-066-3, S. 116–121 (researchgate.net).
  • Hans-Paul Schwefel: Numerical optimization of computer models. Wiley, Chichester 1981, ISBN 0-471-09988-0 (researchgate.net).
  • Wilfried Jakob, Martina Gorges-Schleuter, Christian Blume: Application of Genetic Algorithms to Task Planning and Learning. In: Rheinhard Männer, Bernard Manderick (Hrsg.): Parallel Problem Solving from Nature 2, PPSN-II. North-Holland, Amsterdam 1992, ISBN 0-444-89730-5, S. 291–300 (researchgate.net).
  • Hans-Paul Schwefel: Evolutionsstrategie und numerische Optimierung. Dissertation. Technische Universität, Berlin 1975, doi:10.1007/978-3-0348-5927-1_5 (researchgate.net).
  • Hans-Paul Schwefel: Evolution and Optimum Seeking. Sixth-generation computer technology series. John Wiley & Sons, New York 1995, ISBN 0-471-57148-2 (researchgate.net).
    1. ↑ S. 109
    2. Thomas Bäck, Frank Hoffmeister, Hans-Paul Schwefel: A Survey of Evolution Strategies. In: Richard K. Belew, Lashon B. Booker (Hrsg.): Conf. Proc. of the 4th Int. Conf. on Genetic Algorithms (ICGA'91). Morgan Kaufmann, San Francisco 1991, ISBN 1-55860-208-9, S. 2–9 (researchgate.net).

springer.com (Global: 182nd place; German: 148th place)

link.springer.com

umsl.edu (Global: low place; German: low place)

cs.umsl.edu

  • Cesary Janikow, Zbigniew Michalewicz: An Experimental Comparison of Binary and Floating Point Representations in Genetic Algorithms. In: Conf. Proc of the Fourth Int. Conf. on Genetic Algorithms (ICGA'91). 1991, S. 31–36 (umsl.edu [PDF]).

web.archive.org (Global: 1st place; German: 1st place)

wiley.com (Global: 150th place; German: 246th place)

  • Kaisa Miettinen, Pekka Neittaanmäki, M.M. Mäkelä, Jacques Périaux (Hrsg.): Evolutionary Algorithms in Engineering and Computer Science: Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, Genetic Programming and Industrial Applications. Wiley, Chichester, Weinheim 1999, ISBN 978-0-471-99902-7 (wiley.com).

worldcat.org (Global: 4th place; German: 33rd place)

zdb-katalog.de (Global: 107th place; German: 5th place)

  • Heikki Maaranen, Kaisa Miettinen, Antti Penttinen: On initial populations of a genetic algorithm for continuous optimization problems. In: Journal of Global Optimization. Band 37, Nr. 3, 23. Januar 2007, ISSN 0925-5001, S. 405–436, doi:10.1007/s10898-006-9056-6 (researchgate.net [abgerufen am 1. Oktober 2023]).
  • Thomas Bäck, Hans-Paul Schwefel: An Overview of Evolutionary Algorithms for Parameter Optimization. In: Evolutionary Computation. Band 1, Nr. 1, 1. März 1993, ISSN 1063-6560, S. 1–23, doi:10.1162/evco.1993.1.1.1.
    1. ↑ S. 5
    2. Ralf Mikut, Frank Hendrich: Produktionsreihenfolgeplanung in Ringwalzwerken mit wissensbasierten und evolutionären Methoden. In: Automatisierungstechnik. Band 46, Nr. 1, Januar 1998, ISSN 2196-677X, S. 15–21, doi:10.1524/auto.1998.46.1.15.
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