Abstract
This paper describes several measures that can be useful when analyzing features of search in genetic algorithms. Results obtained by different researchers are often hard to compare and it is often difficult to draw general conclusions from them. This means that non-experienced users of genetic algorithms have little help in constructing their own effective solutions for a particular problem. The article contains a suggestion about possible ways of investigating features of search in genetic algorithms. These features are defined together with descriptions how to measure them.
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T. Bäck. The interaction of mutation rate, selection, and self-adaption within a genetic algorithm. Parallel Problem Solving from Nature, 2:85–94, 1992.
T. Bäck, U. Hammel, and H.-P. Schwefel. Evolutionary computation: Comments on the history and current state. IEEE Transaction on Evolutionary Computation, 1(1), 1997.
T. Bäck and F. Hoffmeister. Extended selection mechanisms in genetic algorithms. In Proceedings of the Fourth International Conference on Genetic Algorithms and their Applications, 1991.
D. Beasley, D. R. Bull, and R. R. Martin. An overview of genetic algorithms: Part 1, fundamentals. University Computing, 15 (2):58–69, 1993.
D. Beasley, D. R. Bull, and R. R. Martin. An overview of genetic algorithms: Part 2, research topics. University Computing, 15(2):170–181, 1993.
D. Beasley, D. R. Bull, and R. R. Martin. Reducing epistatis in combinatorial problems by expansive coding. The Proceeding of the Fifth International Conference on Genetic Algorithms, 1993.
A. E. Eiben, R. Hinterding, and Z. Michalewicz. Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 2(4), 1998.
P. J. Hancock. An empirical comparision of selection methods in evolutionary algorithms. Evolutionary Computing: AISB Workshop, 1994.
K. A. D. Jong and W. M. Spears. A formal analysis of the role of multi-point crossover in genetic algorithms. Annals of Mathematics and Artificial Intelligence Journal, 1992.
S. J. Louis and G. J. E. Rawlins. Predicting convergence time for genetic algorithms. Technical report, University of Waterloo, 1993.
Z. Michalewicz. Genetic algorithms+data structures=evolution programs. Springer, 1996.
Z. Michalewicz, S. Esquivel, R. Gallard, M. Michalewicz, and G. Ta. The spirit of evolutionary algorithms. Journal of Heuristics, 1(2):177–206, 1995.
G. F. Miller. Exploiting mate choice in evolutionary computation: Sexual selection as a process of search, optimization, and diversification. Lecture Notes in Computer Science, 865:65–79, 1994.
M. Mitchell. An introduction to genetic algorithms. MIT Press, 1998.
N. J. Radcliffe. The algebra of genetic algorithms. Annals of Maths and Artificial Intelligence, 1994.
W. M. Spears. Crossover or mutation? Foundations of Genetic Algorithms, 2, 1992.
W. M. Spears. Recombination parameters. Handbook of evolutionary computation, 1995.
W. M. Spears and K. A. D. Jong. An analysis of multi-point crossover. Proceedings of the Foundations of Genetic Algorithms Workshop, 1990.
S. Tsutsui and A. Ghosh. Genetic algorithms with a robust solution searching scheme. IEEE Transaction on Evolutionary Computation, 1(3), 1997.
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© 1999 Springer-Verlag Berlin Heidelberg
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Jonsson, R. (1999). On measures of search features. In: Raś, Z.W., Skowron, A. (eds) Foundations of Intelligent Systems. ISMIS 1999. Lecture Notes in Computer Science, vol 1609. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0095155
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DOI: https://doi.org/10.1007/BFb0095155
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