Öz
In this study, the contribution of changing the metric designated for selecting
the chromosomes in solution of optimization problems in a new evolutionary optimization
algorithm which is called as the Recursive Two-Stage Evolutionary Programming is investigated.
The performed metric change puts forward comparable results in the tested
optimization problems with respect to the metric applied before. The obtained results are
presented in this study.
Anahtar Kelimeler
Genetic algorithm evolutionary programming mutation meta-heuristic methods
Kaynakça
- [1] J. H. Holland, Adaptation in Natural and Artificial Systems, The University of Michigan Press, MI: Ann Arbor 1975.
- [2] J. H. Holland, Genetic Algorithms, Scientific American (July 1992), 66–72.
- [3] D. B. Fogel, Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, IEEE Press, New York 1995.
- [4] K. K. Lai and J.W.M. Chan, Developing a simulated annealing algorithm for the cutting stock problem, Computers and Industrial Engineering 32 (1997), 115–127.
- [5] M. Iwamatsu, Generalized evolutionary programming with Levy-type mutation, Computer Physics Communications 147 (2002), 729–732.
- [6] Q. Wu, Cauchy mutation for decision-making variable of Gaussian particle swarm optimization applied to parameters selection of SVM, Expert Systems with Applications 38 (2011), 4929– 4934.
- [7] Q. Wu, Power load forecasting based on hybrid PSO with Gaussian and adaptive mutation and Ws-SVM, Expert Systems with Applications 37 (2010), 194–201.
- [8] R. Banos, C. Gil, J. Reca and F. G. Montoya, A memetic algoritm applied to the design of water distribution networks, Applied Soft Computing 10 (2010), 261–266.
- [9] M. S. Alam, M. M. Islam, X. Yao and K. Murase, Recurring two-stage evolutionary programming: a noval approach for numerical optimization, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 41 (2011), 1352–1365.
Öz
Kaynakça
- [1] J. H. Holland, Adaptation in Natural and Artificial Systems, The University of Michigan Press, MI: Ann Arbor 1975.
- [2] J. H. Holland, Genetic Algorithms, Scientific American (July 1992), 66–72.
- [3] D. B. Fogel, Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, IEEE Press, New York 1995.
- [4] K. K. Lai and J.W.M. Chan, Developing a simulated annealing algorithm for the cutting stock problem, Computers and Industrial Engineering 32 (1997), 115–127.
- [5] M. Iwamatsu, Generalized evolutionary programming with Levy-type mutation, Computer Physics Communications 147 (2002), 729–732.
- [6] Q. Wu, Cauchy mutation for decision-making variable of Gaussian particle swarm optimization applied to parameters selection of SVM, Expert Systems with Applications 38 (2011), 4929– 4934.
- [7] Q. Wu, Power load forecasting based on hybrid PSO with Gaussian and adaptive mutation and Ws-SVM, Expert Systems with Applications 37 (2010), 194–201.
- [8] R. Banos, C. Gil, J. Reca and F. G. Montoya, A memetic algoritm applied to the design of water distribution networks, Applied Soft Computing 10 (2010), 261–266.
- [9] M. S. Alam, M. M. Islam, X. Yao and K. Murase, Recurring two-stage evolutionary programming: a noval approach for numerical optimization, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 41 (2011), 1352–1365.
Ayrıntılar
| Bölüm | Makaleler |
|---|---|
| Yazarlar | |
| Yayımlanma Tarihi | 1 Mayıs 2013 |
| Yayımlandığı Sayı | Yıl 2013 Cilt: 10 Sayı: 1 |