Abstract
The aim of this paper is to study two new forms of genetic operators: duplication and fabrication. Duplication is a reproduce procedure that will reproduce the best fit chromosome from the elite base. The introduction of duplication operator into the modified GA will speed up the convergence rate of the algorithm however the trap into local optimality can be avoided. Fabrication is an artificial procedure used to produce one or several chromosomes by mining gene structures from the elite chromosome base. Statistical inference by job assignment procedure will be applied to produce artificial chromosomes and these artificial chromosomes provides new search directions and new solution spaces for the modified GA to explore. As a result, better solution quality can be achieved when applying this modified GA. Different set of problems will be tested using modified GA by including these two new operators in the procedure. Experimental results show that the new operators are very informative in searching the state space for higher quality of solutions.
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References
Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming – An Introduction. On the Automatic Evolution of Computer Programs and its Applications Kaufmann. Morgan Kaufmann, CA (1998)
Davis, L.: Adapting Operator Probabilities in Genetic Algorithms. In: Schaffer, J.D. (ed.) Proceedings of the Third International Conference on Genetic Algorithms, pp. 61–69. Morgan Kaufmann, CA (1989)
Jong, K.A.D., Spears, W.M.: A Formal Analysis of the Role of Multi-Point Crossover in Genetic Algorithms. Annals of Mathematics and Artificial Intelligence 5(1), 1–26 (1992)
D’Haeseleer, P.: Context Preserving Crossover in Genetic Programming. In: Proceedings of the 1994 IEEE World Congress on Computational Intelligence, vol. 1, pp. 256–261 (1993)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company, Inc., Reading (1989)
Mathias, K., Whitley, D.: Genetic Operators, the Fitness Landscape and the Traveling Salesman Problem. In: Männer, R., Manderick, B. (eds.) Proceedings of Parallel Problem Solving from Nature, vol. 2, pp. 219–228 (1992)
Mitchell, M., Forrest, S.: Genetic Algorithms and Artificial Life. Artificial Life 1(3), 267–289 (1994)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)
Schaffer, J.D., Eshelman, L.J.: On Crossover asan Evolutionarily Viable Strategy. In: Belew, R.K., Booker, L.B. (eds.) Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 61–68 (1991)
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Chang, PC., Wang, YW., Liu, CH. (2005). New Operators for Faster Convergence and Better Solution Quality in Modified Genetic Algorithm. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_137
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DOI: https://doi.org/10.1007/11539117_137
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