Abstract
This paper presents a new approach to function optimisation using a new variant of GAs. This algorithm is called the Stud GA. Instead of stochastic selection, the fittest individual, the Stud, shares its genetic information with all others using simple GA operators. The standard Gray coding is maintained. Simple techniques are added to maintain diversity of the population and help achieve the global optima in difficult multimodal search spaces. The benefits of this approach are an improved performance in terms of accuracy, efficiency and reliability. This approach appears to be able to deal with a wide array of functions and to give consistent repeatability of optimisation performance. A variety of test functions is used to illustrate this approach. Results presented suggest a viable and attractive addition to the portfolio of evolutionary computing techniques.
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References
Bohachevsky, I. O., Johnson, M. E., and Stein, M. L.: Generalised Simulated Annealing for Function Optimization. Technometrics. Vol 28:3, pp. 209–218. [in 3](
De Falco, I., Del Balio, R., Della Cioppa, A, Tarantino, E.,.: A comparative Study of Evolutionary Algorithms for Function Optimisation. Proceedings of the Second (WWW) Workshop on Evolutionary Computing (WEC2), Nagoya, Japan (1996)
Fogel, D.: Evolutionary Computing, Toward a New Philosophy of Machine Intelligence. IEEE Press, Piscataway, NJ (1995)
Keane, A.,: Experiences with Optimizers. In: Proceedings of the IMechE/IEE Symposium on Genetic Algorithms in Design Optimisation. London, UK (January 31, 1996).
Khatib W. and Fleming P.J., Evolutionary computing for multidisciplinary optimisation, Proc 2nd IEE/IEEE International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications GALESIA 97, Glasgow, 1997, pp. 7–12.
Muhlenbein, H., and Schilierkamp-Voosen, D.: Predictive Models for The Breeder Genetic Algorithm I. Continuous Parameter Optimization. Evolutionary Computation, Vol 1, No. 1, pp. 25–49 (1993).[in 2]
Scott Gordon, V., and Whitley, D.: Serial and Parallel Genetic Algorithms as Function Optimizers. In: Proceedings of the 5th International Conference on Genetic Algorithms, (Forrest, S. Ed.), pp. 177–183, Morgan-Kaufmann (1993).
Schraudolph, N.N., and Belew, R.K.: Dynamic Parameter Encoding for Genetic Algorithms. In Machine Learning, Vol. 9, Iss. 1, pp. 9–21, The Netherlands (June 1992).
Storn, R., and Price, K.: Minimizing the Real Function of the ICEC'96 Contest by Differential Evolution. Int. Conference on evolutionary Computation, Nagoya, Japan (1996).
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© 1998 Springer-Verlag Berlin Heidelberg
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Khatib, W., Fleming, P.J. (1998). The stud GA: A mini revolution?. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN V. PPSN 1998. Lecture Notes in Computer Science, vol 1498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056910
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DOI: https://doi.org/10.1007/BFb0056910
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