Abstract
The optimization of structures is important for many industrial applications. But the problem of structure optimization is hardly understood. In the field of evolutionary computation mostly syntactical (pure structure-based) variation operators are used. For this kind of variation operators it is difficult to integrate domain-knowledge and to control the size of a mutation step. To gain insight into the basic problems of structure optimization we analyze mutation operators for evolutionary programming. For a synthetic problem we are able to derive a semantical mutation operator. The semantical mutation operator makes use of domain knowledge and has a well-defined parameter to adjust the step size.
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References
K. Chellapilla. Evolving computer programs without subtree crossover. IEEE Transactions on Evolutionary Computation, 1(3):209–216, 1997.
P. J. Denning, J. B. Dennis, and J. E. Qualitz. Machines, Languages, and Computation. Prentice-Hall, Englewood Cliffs, 1979.
S. Droste and D. Wiesmann. Metric based evolutionary algorithms. In R. Poli, W. Banzhaf, W. B. Langdon, J. F. Miller, P. Nordin, and T. C. Fogarty, editors, Genetic Programming, Proc. of EuroGP’2000, Edinburgh, April 15-16, 2000, volume 1802 of LNCS, pages 29–43, Berlin, 2000. Springer.
M. Emmerich, M. Grötzner, and M. Schütz. Design of graph-based evolutionary algorithms: A case study of chemical process networks. Evolutionary Computation,9(3):329–354, 2001.
D. B. Fogel. Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. IEEE Press, New York, 1995.
L. J. Fogel, A. J. Owens, and M. J. Walsh. Artificial Intelligence through Simulated Evolution. Wiley, New York, 1966.
M. R. Garey and D. S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman, New York, 1979.
G. Rudolph. Convergence Properties of Evolutionary Algorithms. Verlag Dr. Kovač, Hamburg, 1997.
G. Rudolph. Finite Markov chain results in evolutionary computation: A tour d’horizon. Fundamenta Informaticae, 35(1–4):67–89, 1998.
B. Sendhoff. Evolution of Structures: Optimization of Artificial Neural Structures for Information Processing. Shaker, Aachen, 1998.
T. Slawinski, A. Krone, U. Hammel, D. Wiesmann, and P. Krause. A hybrid evolutionary search concept for data-based generation of relevant fuzzy rules in high dimensional spaces. In Proc. of the Eighth Int’l Conf. on Fuzzy Systems (FUZZ-IEEE’99), Seoul, Korea, Aug. 22–25, 1999, pages 1431–1437, Piscataway, NJ, 1999. IEEE Press.
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Wiesmann, D. (2002). From Syntactical to Semantical Mutation Operators for Structure Optimization. In: Guervós, J.J.M., Adamidis, P., Beyer, HG., Schwefel, HP., Fernández-Villacañas, JL. (eds) Parallel Problem Solving from Nature — PPSN VII. PPSN 2002. Lecture Notes in Computer Science, vol 2439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45712-7_23
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DOI: https://doi.org/10.1007/3-540-45712-7_23
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