Abstract
The field of evolutionary computation has grown significantly in the past decade and has matured in both its theoretical and its application areas. In this paper we provide a brief introduction to the field, summarize the important areas of application, and give a brief indication of where the field is headed.
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References
L. Rechenberg: Cybernetic solution path of an experimental problem. Royal Aircraft Establish., library trans. 1122, Hants, U.K.: Farnborough (1965).
L. Rechenberg: Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Stuttgart: Frommann-Holzboog (1973).
H.-P. Schwefel: Kybernetische Evolution als Strategie der experimentellen Forschung in der Stroemungstechnik. Diploma thesis, Technical University of Berlin (1965).
H.-P. Schwefel: Numerical optimization of computer models. Chichester: Wiley (1981).
L. Fogel, J. Owens, & M. Walsh: Artificial intelligence through simulated evolution. New York: Wiley (1966).
D. Fogel: Evolving Artificial Intelligence. Doctoral thesis, University of California, San Diego (1992).
J. Holland: Outline of a logical theory of adaptive systems. J. of ACM, 3, 297–314 (1962).
J. Holland: Adaptation in natural and artificial systems. Ann Arbor: The University of Michigan Press (1975), second edition Cambridge, Mass: MIT Press (1992).
K. De Jong: An analysis of the behavior of a class of genetic adaptive systems. Doctoral thesis, University of Michigan, Ann Arbor (1975).
D. Goldberg: Genetic Algorithms in search, optimization, and machine learning. Reading, MA: Adison-Wesley (1987).
T. Baeck & H.-P. Schwefel: An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation 1, 1–24 (1993).
L. Davis: Handbook of genetic algorithms. Van Nostrand Reinhold (1991).
Z. Michalewicz: Genetic algorithms+ data structures= evolution programs. Berlin: Springer-Verlag (1992).
S. Forrest (ed.): Proceedings of the fifth international conference on genetic algorithms. San Mateo, CA: Morgan Kaufmann (1993).
J. Grefenstette: Learning sequential decision rules using simulation models and competition. Machine Learning 5, 4, 355–382 (1992).
IEEE (ed.): Proceedings of the international joint conference on neural networks. Piscataway, NJ: IEEE Service Center (1993).
J. Schaffer & D. Whitley (eds.): Proceedings of the workshop on combinations of genetic algorithms and neural networks. Los Alamitos, CA: IEEE Computer Society Press (1992).
J. Koza: Genetic Programming: On the programming of computers by means of natural selection. Cambridge, MA: MIT Press (1992).
T. Davis & J. Principe: A Markov chain framework for the simple genetic a lgorithm. Evolutionary Computation 1:3, 191–212 (1993).
M. Vose: Modeling simple genetic algorithms. Proceedings of the second workshop on the foundations of genetic algorithms, San Mateo, CA: Morgan Kaufmann (1992).
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© 1994 Springer-Verlag Berlin Heidelberg
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De Jong, K. (1994). An Introduction to Evolutionary Computation and Its Applications. In: Reusch, B. (eds) Fuzzy Logik. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-79386-8_40
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DOI: https://doi.org/10.1007/978-3-642-79386-8_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-58649-4
Online ISBN: 978-3-642-79386-8
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