Abstract
Evolution Strategies (ESs) and Genetic Algorithms (GAs) are compared in a formal as well as in an experimental way. It is shown, that both are identical with respect to their major working scheme, but nevertheless they exhibit significant differences with respect to the details of the selection scheme, the amount of the genetic representation and, especially, the self-adaptation of strategy parameters.
Preview
Unable to display preview. Download preview PDF.
References
James Edward Baker. Adaptive selection methods for genetic algorithms. In J. J. Grefenstette, editor, Proceedings of the first international conference on genetic algorithms and their applications, pages 101–111, Hillsdale, New Jersey, 1985. Lawrence Erlbaum Associates.
J. Born. Evolutionsstrategien zur numerischen Lösung von Adaptationsaufgaben. Dissertation A, Humboldt-Universität, Berlin, GDR, 1978.
Richard A. Caruna, Larry J. Eshelman, and J. David Schaffer. Representation and hidden bias II: Eliminating defining length bias in genetic search via shuffle crossover. In N. S. Sridharan, editor, Eleventh international joint conference on artificial intelligence, pages 750–755. Morgan Kaufmann Publishers, August 1989.
Kalyanmoy Deb and David E. Goldberg. An investigation of niche and species formation in genetic function optimization. In J. David Schaffer, editor, Proceedings of the third international conference on genetic algorithms and their applications, pages 42–50. Morgan Kaufmann Publishers, 1989.
Larry L. Eshelman, Richard A. Caruna, and J. David Schaffer. Biases in the crossover landscape. In J. David Schaffer, editor, Proceedings of the third international conference on genetic algorithms and their applications, pages 10–19. Morgan Kaufmann Publishers, 1989.
John J. Grefenstette and James E. Baker. How genetic algorithms work: A critical look at implicit parallelism. In J. David Schaffer, editor, Proceedings of the third international conference on genetic algorithms and their applications, pages 20–27. Morgan Kaufmann Publishers, 1989.
David E. Goldberg. Genetic algorithms in search, optimization and machine learning. Addison Wesley, 1989.
David E. Goldberg and J. Richardson. Genetic algorithms with sharing for multimodal function optimization. In J. J. Grefenstette, editor, Genetic Algorithms and their Applications, pages 41–49, Hillsdale, New Jersey, 1987. Lawrence Erlbaum Associates.
John J. Grefenstette. Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics, SMC-16(1):122–128, 1986.
John J. Grefenstette. A User's Guide to GENESIS. Navy Center for Applied Research in Artificial Intelligence, Washington, D. C., 1987.
Frank Hoffmeister and Thomas Bäck. Genetic algorithms and evolution strategies: Similarities and differences. Technical Report “Grüne Reihe” No. 365, Department of Computer Science, University of Dortmund, November 1990.
John H. Holland. Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor, 1975.
Kenneth De Jong. An analysis of the behaviour of a class of genetic adaptive systems. PhD thesis, University of Michigan, 1975. Diss. Abstr. Int. 36(10), 5140B, University Microfilms No. 76-9381.
Ingo Rechenberg. Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Friedrich Frommann Verlag, Stuttgart, 1973.
Günter Rudolph. Globale Optimierung mit Parallelen Evolutionsstrategien. Diploma thesis, University of Dortmund, Department of Computer Science, Dortmund, FRG, July 1990.
J. David Schaffer, Richard A. Caruna, Larry J. Eshelman, and Rajarshi Das. A study of control parameters affecting online performance of genetic algorithms for function optimization. In J. David Schaffer, editor, Proceedings of the third international conference on genetic algorithms and their applications, pages 51–60. Morgan Kaufmann Publishers, 1989.
Hans-Paul Schwefel. Evolutionsstrategie und numerische Optimierung. Dissertation, Technische Universität Berlin, Berlin, May 1975. Identical to Schw77 and Schw81.
Hans-Paul Schwefel. Numerische Optimierung von Computer-Modellen mittels der Evolutionsstrategie. Interdisciplinary systems research; 26. Birkhäuser, Basel, FRG, 1977.
Hans-Paul Schwefel. Numerical Optimization of Computer Models. Wiley, Chichester, 1981.
Hans-Paul Schwefel. Optimum seeking methods: Subroutines for the minimization of non-linear functions of several variables by means of direct (derivative-free) methods. Interner Bericht KFA-STE-IB-7/81, Kernforschungsanlage Jülich GmbH, Jülich, FRG, October 1981.
Hans-Paul Schwefel. Collective phenomena in evolutionary systems. In 31st Annual Meeting of the International Society for General System Research, Budapest, pages 1025–1033, June 1987.
Gilbert Syswerda. Uniform crossover in genetic algorithms. In J. David Schaffer, editor, Proceedings of the third international conference on genetic algorithms and their applications, pages 2–9. Morgan Kaufmann Publishers, 1989.
A. Törn and A. Zilinskas. Global Optimization, volume 350 of Lecture Notes in Computer Science. Springer, Berlin, FRG, 1989.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1991 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hoffmeister, F., Bäck, T. (1991). Genetic Algorithms and evolution strategies: Similarities and differences. In: Schwefel, HP., Männer, R. (eds) Parallel Problem Solving from Nature. PPSN 1990. Lecture Notes in Computer Science, vol 496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0029787
Download citation
DOI: https://doi.org/10.1007/BFb0029787
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-54148-6
Online ISBN: 978-3-540-70652-6
eBook Packages: Springer Book Archive