Abstract
For the calibration of laser induced plasma spectrometers robust and efficient local search methods are required. Therefore, several local optimizers from nonlinear optimization, random search and evolutionary computation are compared. It is shown that evolutionary algorithms are superior with respect to reliability and efficiency. To enhance the local search of an evolutionary algorithm a new method of random memorizing is introduced. It leads to a substantial gain in efficiency for a reliable local search.
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References
Ingo Rechenberg, Evolutionsstrategie’ 94, Frommann-Holzboog, 1994.
Adam Ghozeil and David B. Fogel, A Preliminary Investigation into Directed Mutations in Evolutionary Algorithms, In: H.-M. Voigt, W. Ebeling, I. Rechenberg, and H.-P. Schwefel (Eds.). Parallel Problem Solving from Nature — PPSN IV, 329–335. Lecture Notes in Computer Science 1141. Springer, 1996
Hans-Paul Schwefel, Evolution and Optimum Seeking, Wiley, 1995.
Hans-Georg Beyer, Towards a Theory of Evolution Strategies: Some Asymptotical Results from the (1;+ λ) - Theory, Evolutionary Computation 1(2) 165–188, 1993.
Nikolaus Hansen and Andreas Ostermeier, Adapting Arbitrary Normal Mutation Distributions in Evolution Strategies: The Covariance Matrix Adaptation In: Proceedings of the 1996 IEEE Int. Conf. on Evolutionary Computation, 312–317. IEEE Press, 1996
Nikolaus Hansen, Andreas Ostermeier, and Andreas Gawelczyk, On the Adaptation of Arbitrary Normal Mutation Distributions in Evolutions Strategies: The Generating Set Adaptation In: L.J. Eshelman (Ed.). Proceedings of the Sixth Int. Conf. on Genetic Algorithms, 57–64, Morgan Kaufmann, 1995
Deniz Yuret and Michael de la Maza, Dynamic Hillclimbing: Overcoming the Limitations of Optimization Techniques. In: The Second Turkish Symposium on Artificial Intelligence and Neural Networks, 208–212, 1993
Hans-Michael Voigt, Heinz Mühlenbein, and Dragan Cvetcovic, Fuzzy Recombination for the Breeder Genetic Algorithm. In: L.J. Eshelman (Ed.). Proceedings of the Sixth Int. Conf. on Genetic Algorithms, 104–113, Morgan Kaufmann, 1995
Hans-Michael Voigt and Heinz Mühlenbein, Gene Pool Recombination and the Utilization of Covariances for the Breeder Genetic Algorithm In: Proceedings of the 1995 IEEE Int. Conf. on Evolutionary Computation, 172–177. IEEE Press, 1995
J.A. Nelder and R. Mead, A Simplex Method for Function Minimization, Comp. J. 7, 308–313, 1965
G.W. Stewart, A Modification of Davidon’s Minimization Method to Accept Difference Approximations of Derivatives JACM 14, 72–83, 1967
M.J.D. Powell, An Effcient Method for Finding the Minimum of a Function of Several Variables without Calculating Derivatives Comp. J. 7, 155–162, 1964
Francisco J. Solis and Roger J.-B. Wets, Minimization by Random Search Techniques Operations Research, 19–30, 1981
William H. Press, Saul A. Teukolsky, William T. Vetterling, and Brian P. Flannery, Numerical Recipes in C, 2nd Edition Cambridge University Press, 1992
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Voigt, HM., Lange, J.M. (1998). On the Benefits of Random Memorizing in Local Evolutionary Search. In: Polkowski, L., Skowron, A. (eds) Rough Sets and Current Trends in Computing. RSCTC 1998. Lecture Notes in Computer Science(), vol 1424. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-69115-4_35
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DOI: https://doi.org/10.1007/3-540-69115-4_35
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