Using Memetic Algorithms for the Solution of Technical Problems
The intention of this paper is, to help the user of evolutionary algorithms to adapt them easier to their problem at hand. For a lot of problems in the technical field it is not necessary to reach an optimum solution, but to reach a good solution in time. In many cases the solution is undetermined or there doesn-t exist a method to determine the solution. For these cases an evolutionary algorithm can be useful. This paper intents to give the user rules of thumb with which it is easier to decide if the problem is suitable for an evolutionary algorithm and how to design them.
[1] K. Weicker, Evolution¨are Algorithmen, 1st ed. Teubner Verlag, 2002.
[2] J. H. Holland, Adaption in natural and artifical systems. University of
Michigan Press, Ann Arbor, 1975.
[3] K. A. D. Jong, "An analysis of the behavior of a class of genetic adaptive
systems," Ph.D. dissertation, University of Michigan, 1975.
[4] I. Rechenberg, Evolutionsstrategie. Optimierung technischer Systeme
nach Prinzipien der biologischen Evolution, 1st ed. Frommann-
Holzboog, 1973.
[5] H.-P. Schwefel, "Evolutionsstrategie und numerische optimierung,"
Ph.D. dissertation, Technische Universitt Berlin, 1975.
[6] L. J. Fogel, A. J. Owens, and M. J. Walsh, Artifical intelligence through
simulated evolution. Wiley, New York, 1966.
[7] D. B. Fogel, "Evolving artifical intelligence," Ph.D. dissertation, University
of California, San Diego, 1992.
[8] J. R. Koza, Genetic programming: A paradigm for genetically breeding
populations of computer programs to solve problems. Stanford
University Computer Science Department, 1990.
[9] R. Dawkins, The selfish gene, new ed. Oxford New York: Oxford
University Press, 1989.
[10] . E. Eiben, R. Hinterding, and Z. Michalewicz, Parameter Control in
Evolutionary Algorithms. IEEE Computer Society, 1999.
[11] I. Gerdes, F. Klawonn, and R. Kruse, Evolutionre Algorithmen, 1st ed.
Vieweg Verlag, 2004.
[12] E. Schneburg, F. Heinzmann, and S. Feddersen, Genetische Algorithmen
und Evolutionsstrategien, 1st ed. Addison-Wesley, 1994.
[13] A. van Gelder, Approximate simulation of elastic membranes by triangulated
spring meshes. J. Graph. Tools, 3(2):21-42, 1998.
[14] A. Nealen, M. M¨uller, R. Keiser, E. Boxermann, M. Carlson, D. W.,
and C. Helmberg, "Physically based deformable models in computer
graphics," cgforum, pp. 71-94, Jul 2005.
[15] G. Bianchi, B. Solenthaler, G. Szkely, and M. Harders, "Simultaneous
topology and stiffness identification for mass-spring models based on
fem reference deformations," 2004.
[16] U. V¨ollinger, H. Stier, J. Priesnitz, and F.-L. Krause, "Evolutionary
optimization of mass-spring models," CIRP Journal of Manufacturing
Science and Technology, vol. 1, pp. 137-141, December 2008.
[17] O. Deussen, Untersuchung effizienter Verfahren zur Bewegungssimulation
deformierbarer K¨orper, fortschritt-berichte vdi reihe 20 nr. 215 ed.
VDI Verlag, 1996.
[18] V. D. I. VDI, Konstruktionsmethodik - Technisch-wirtschaftliche Bewertung,
vdi richtlinie 2225, blatt 3 ed., 1998.
[1] K. Weicker, Evolution¨are Algorithmen, 1st ed. Teubner Verlag, 2002.
[2] J. H. Holland, Adaption in natural and artifical systems. University of
Michigan Press, Ann Arbor, 1975.
[3] K. A. D. Jong, "An analysis of the behavior of a class of genetic adaptive
systems," Ph.D. dissertation, University of Michigan, 1975.
[4] I. Rechenberg, Evolutionsstrategie. Optimierung technischer Systeme
nach Prinzipien der biologischen Evolution, 1st ed. Frommann-
Holzboog, 1973.
[5] H.-P. Schwefel, "Evolutionsstrategie und numerische optimierung,"
Ph.D. dissertation, Technische Universitt Berlin, 1975.
[6] L. J. Fogel, A. J. Owens, and M. J. Walsh, Artifical intelligence through
simulated evolution. Wiley, New York, 1966.
[7] D. B. Fogel, "Evolving artifical intelligence," Ph.D. dissertation, University
of California, San Diego, 1992.
[8] J. R. Koza, Genetic programming: A paradigm for genetically breeding
populations of computer programs to solve problems. Stanford
University Computer Science Department, 1990.
[9] R. Dawkins, The selfish gene, new ed. Oxford New York: Oxford
University Press, 1989.
[10] . E. Eiben, R. Hinterding, and Z. Michalewicz, Parameter Control in
Evolutionary Algorithms. IEEE Computer Society, 1999.
[11] I. Gerdes, F. Klawonn, and R. Kruse, Evolutionre Algorithmen, 1st ed.
Vieweg Verlag, 2004.
[12] E. Schneburg, F. Heinzmann, and S. Feddersen, Genetische Algorithmen
und Evolutionsstrategien, 1st ed. Addison-Wesley, 1994.
[13] A. van Gelder, Approximate simulation of elastic membranes by triangulated
spring meshes. J. Graph. Tools, 3(2):21-42, 1998.
[14] A. Nealen, M. M¨uller, R. Keiser, E. Boxermann, M. Carlson, D. W.,
and C. Helmberg, "Physically based deformable models in computer
graphics," cgforum, pp. 71-94, Jul 2005.
[15] G. Bianchi, B. Solenthaler, G. Szkely, and M. Harders, "Simultaneous
topology and stiffness identification for mass-spring models based on
fem reference deformations," 2004.
[16] U. V¨ollinger, H. Stier, J. Priesnitz, and F.-L. Krause, "Evolutionary
optimization of mass-spring models," CIRP Journal of Manufacturing
Science and Technology, vol. 1, pp. 137-141, December 2008.
[17] O. Deussen, Untersuchung effizienter Verfahren zur Bewegungssimulation
deformierbarer K¨orper, fortschritt-berichte vdi reihe 20 nr. 215 ed.
VDI Verlag, 1996.
[18] V. D. I. VDI, Konstruktionsmethodik - Technisch-wirtschaftliche Bewertung,
vdi richtlinie 2225, blatt 3 ed., 1998.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:55493", author = "Ulrike Völlinger and Erik Lehmann and Rainer Stark", title = "Using Memetic Algorithms for the Solution of Technical Problems", abstract = "The intention of this paper is, to help the user of evolutionary algorithms to adapt them easier to their problem at hand. For a lot of problems in the technical field it is not necessary to reach an optimum solution, but to reach a good solution in time. In many cases the solution is undetermined or there doesn-t exist a method to determine the solution. For these cases an evolutionary algorithm can be useful. This paper intents to give the user rules of thumb with which it is easier to decide if the problem is suitable for an evolutionary algorithm and how to design them.
", keywords = "Multi criteria optimization, Memetic algorithms", volume = "3", number = "11", pages = "966-6", }
