Abstract
In order to apply genetic algorithms (GAs) successfully to a given problem one has to find a good representation for potential solutions to that problem. Roughly speaking a good representation is one where building blocks for the problem's solution are relatively insensitive to crossover disruption, i.e., the building blocks have short defining lengths. However, such a representation is difficult to find if heuristic or background knowledge about the problem is lacking.
In this paper, it is shown how the GA itself can be used to first search for a good representation for a given problem and subsequently solves that problem using the found representation. The main question we have to address is how a representation can be evaluated without solving the problem first. The hypothesis put forward is that a good representation is one for which crossover shows a high correlation between the fitnesses of parents and their offspring.
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References
Belew R. K. & Booker, L. B. (Eds.) (1991). Proceedings of the Fourth International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA.
K. A. De Jong. An Analysis of the Behavior of a Class of Genetic Adaptive Systems. PhD thesis, University of Michigan, 1975.
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison Wesley, Reading, MA.
Goldberg, D. E. (1989). Messy Genetic Algorithms: Analysis and First Results. Complex Systems, 3(5), 1989.
J. H. Holland. Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, 1975.
G. E. Liepins and M. D. Vose. Representational issues in genetic optimization. Journal of Experimental and Theoretical Artificial Intelligence, 2(2), 1990.
Manderick, B., de Weger, M., & Spiessens, P. (1991). The genetic algorithm and the structure of the fitness landscape. In [1].
Manderick, B. & Spiessens, P. (1989). Fine-grained parallel genetic algorithms. In [9].
J. D. Schaffer, editor. Proceedings of the Third International Conference on Genetic Algorithms (ICGA-89). Morgan Kaufmann, San Mateo, 1989.
Piet Spiessens. Fine-Grained Parallel Genetic Algorithms: Analysis and Applications. PhD thesis, Free University of Brussels, AI Laboratory, 1994.
Spiessens, P. & Manderick, B. (1990). A genetic algorithm for massively parallel computers. In Proceedings of the International Conference on Parallel Processing in Neural Systems and Computers (ICNC-90).
Spiessens, P. & Manderick, B. (1991). A massively parallel genetic algorithm: Implementation and first analysis. In [1].
D. Whitley, T. Starkweather, and D. Fuquay. Scheduling problems and traveling salesmen: The genetic edge recombination operator. In [9].
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© 1996 Springer-Verlag Berlin Heidelberg
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Spiessens, P., Manderick, B. (1996). Finding optimal representations using the crossover correlation coefficient. In: Borges, D.L., Kaestner, C.A.A. (eds) Advances in Artificial Intelligence. SBIA 1996. Lecture Notes in Computer Science, vol 1159. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61859-7_10
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DOI: https://doi.org/10.1007/3-540-61859-7_10
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