Abstract
To design crossover operators with high search ability in real-coded Genetic Algorithms, it will be efficient to utilize both information regarding the parent distribution and the landscape of the objective function. Here, we propose a new offspring generation method using Delaunay triangulation. The proposed method can concentrate offspring in regions with a satisfactory evaluation value, inheriting the parent distribution. Through numerical examples, the proposed method was shown to be capable of deriving the optimum with a smaller population size and lower number of evaluations than Simplex Crossover, which uses only information of the parent distribution.
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References
Eshleman, L., Schaffer, J.D.: Real-Coded Genetic Algorithms and Interval-Schemata. Foundations of Genetic Algorithms 2 (1993)
Ono, I., Kobayashi, S.: A Real Coded Genetic Algorithm for Function Optimization Using Unimodal Normal Distributed Crossover. In: Proc. of the 7th International Conference on Genetic Algorithms (1997)
Kita, H., Ono, I., Kobayashi, S.: Multi-parental Extension of the Unimodal Normal Distribution Crossover for Real-coded Genetic Algorithm. In: Proc. of the 1999 Congress on Evolutionary Computation (1999)
Tsutsui, S., Yamamura, M., Higuchi, T.: Multi-parent Recombination with Simplex Crossover in Real-Coded Genetic Algorithms. In: GECCO 1999 (1999)
Higuchi, T., Tsutsui, S., Yamamura, M.: Theoretical Analysis of Simplex Crossover for Real-Coded Genetic Algorithms. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, Springer, Heidelberg (2000)
Kita, H., Yamamura, M.: A Functional Specialization Hypothesis for Designing Genetic Algorithms. In: Proc. of the IEEE International Conference on Systems, Man and Cybernetics (1999)
Pelikan, M., Goldberg, D.E., Lobo, F.: A Survey of Optimization by Building and Using Probabilistic Models. Technical Report 99018, IlliGAL (1999)
Larranaga, P., Lozano, J.A.: Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation. Kluwer Academic Publishers, Dordrecht (2001)
Bosman, P., Thierens, D.: Continuous Iterated Density Estimation Evolutionary Algorithms Within The IDEA Framework. In: Proc. of OBUPM Workshop at the GECCO 2000, pp. 197–200 (2000)
Tsutsui, S., Pelikan, M., Goldberg, D.E.: Evolutionary Algorithm Using Marginal Histograms in Continuous Domain. Technical Report 2001019, IlliGAL (2001)
Yuan, B., Gallagher, M.: Playing in Continuous Spaces: Some Analysis and Extension of Population-based Incremental Learning. In: Proc. of the 2003 Congress on Evolutionary Computation, pp. 443–450 (2003)
Okabe, A., Boots, B., Sugihara, K.: Spatial Tesselation Concept and Applications of Voronoi Diagrams. John Wiley & Sons, New York (1992)
Bradford Barber, C., Dobkin, D.P., Huhdanpaa, H.: The Quickhull Algorithm for Convex Hulls. ACM Transactions on Mathematical Software 22(4), 469–483 (1996)
Qhull code for Convex Hull, Delaunay Triangulation, Voronoi Diagram, and Halfspace Intersection about a Point, http://www.qhull.org/
Satoh, H., Ono, O., Kobayashi, S.: Minimal Generation Gap Model for GAs considering Both Exploration and Exploitation. In: Proc. of IIZUKA 1996 (1996)
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© 2006 Springer-Verlag Berlin Heidelberg
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Shimosaka, H., Hiroyasu, T., Miki, M. (2006). Offspring Generation Method Using Delaunay Triangulation for Real-Coded Genetic Algorithms. In: Runarsson, T.P., Beyer, HG., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds) Parallel Problem Solving from Nature - PPSN IX. PPSN 2006. Lecture Notes in Computer Science, vol 4193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11844297_84
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DOI: https://doi.org/10.1007/11844297_84
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38990-3
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