1. Holland, J.H., Adaption in natural and artificial
systems, University of Michigan Press, Ann
Arbor, 1975.
2. De Jong, K.A., Spears, W.M., “An analysis of the
interacting roles of population size and crossover
in genetic algorithms”, Parallel Problem
Solving from Nature, Springer, Berlin, Cilt
LNCS 496, 38-47, 1991.
3. Schaffer, J.D., Caruana, R.A., Eshelman, L.J.,
Das, R., “A study of control parameters affecting
online performance of genetic algorithms for
function optimization”, The 3rd Int. Conf. GAs,
51-60, 1989.
4. Grefenstette, J.J., “Optimisation of control
parameters for genetic algorithms”, IEEE Trans.
SMC, Cilt 16, No 1, 122-128, 1986.
5. Reeves C.R., Rowe J.E., Genetic algorithms
principles and perspectives: A Guide to GA
Theory, ISBN 978-1402072406, Springer, 2002.
6. Michalewicz, Z., Schoenauer, M., “Evolutionary
algorithms for constrained parameter
Baskın Gen Seçimi Operatörüne Dayalı Genetik… A. Kalınlı ve Ö. Aksu
Gazi Üniv. Müh. Mim. Fak. Der. Cilt 26, No 4, 2011 875
optimization problems”, Evolutionary
Computation, Cilt 4, 1-32, 1996.
7. De Jong, K. A., An analysis of the behavior of a
class of genetic adaptive systems, Doctoral
Thesis, Department of Computer and
Communication Sciences. University of
Michigan, Ann Arbor, 1975.
8. Grefenstette, J.J., “Optimization of control
parameters for genetic algorithms”, IEEE Trans
on Systems, Man and Cybernetics, Cilt SMC-
16, 122-128, 1986.
9. Schaffer, J.D., et al, “A study of control
parameters affecting online performance of
genetic algorithms for function optimization”,
3rd International Conference on Genetic
Algorithms, Morgan Kaufmann, San Mateo, CA,
51-60, 1989.
10. Eshelmvn, L.J., Shaffer, J.D., “Preventing
premature convergence in genetic algorithms by
preventing incest”, Proc. 4 th Int. Conf Genetic
Algorithms, 115-122, 1991.
11. Andris, P., Frollo, I., “Optimisation of NMR coils
by genetic algorithms”, Measurement Science
Review, Cilt 2, No 2, 13-22, 2002.
12. Davidor, Y., “Analogous crossover operator”,
Proc 3rd Int.Conf Genetic Algorithms, George
Mason Univ., Arlington, VA., 98-103, 1989.
13. Fagarty, T.C., “Varing the probability of
mutation in the genetic algorithm”, Proc. 3rd
Int. Conf Genetic Algorithms, 104-109, 1989.
14. Watanabe, M., Ida, K., Gen, M., “A genetic
algorithm with modified crossover operator and
search area adaptation for the job-shop
scheduling problem”, Computers and Industrial
Engineering, Cilt 48, No 4, 743-752, 2005.
15. Jiri, K., Jiri, L., “A new genetic operator
maintaining population diversity”, In Dubois
Daniel M., editor(s), Computing Anticipatory
Systems, Liege, Belgium, American Institute of
Physics, ISBN 0-7354-0012-1, 338-348, 2001.
16. Yang, W.X., “An improved genetic algorithm
adopting immigration operator”, Intelligent Data
Analysis, Cilt 8, No 4, 385-401, 2004.
17. Silva, R.R., Lopes, H.S., Erig Lima, C.R., “A
new mutation operator for the elitism-based
compact genetic algorithm”, LNCS, Cilt 4431,
Springer Berlin/Heidelberg, 159-166, 2007.
18. Mitchell, G.G., O'Donoghue, D., Barnes, D.,
McCarville, M., “GeneRepair - A repair operator
for genetic algorithms”, late-breaking paper,
GECCO, Chicago IL, July 2003.
19. Srinwa, M., Patnaik, L.M., “Adaptive
probabilities of crossover and mutation in genetic
algorithms”, IEEE Trans. SMC, Cilt SMC-24,
No 4, 656-666, 1994.
20. Grefenstette, J.J., “Qptimization of control
parameters for genetic algorithms”, IEEE Trans.
SMC, Cilt SMC-16, No l, 122-128, 1986.
21. Samples, M.E., Byom, M.J., Diada, J.M.,
“Parameter sweeps for exploring parameter
spaces of genetic and evolutionary algorithms”,
in Parameter Setting in Evolutionary
Algorithms, Fernando G. Lobo, Cl´audio F.
Lima, Zbigniew Michalewicz (Editors), Springer,
161-184, 2007.
22. Holland J.H., “Building blocks, cohort genetic
algorithms, and hyperplane-defined functions”,
Evolutionary Computation, Cilt 8, 373-391,
2000.
23. Aksu, O., Yeni bir paralel genetik algoritma
modeli ve analog devre tasarımına
uygulanması, Yüksek Lisans Tezi, Erciyes
Üniversitesi Bilgisayar Mühendisliği Anabilim
Dalı, Kayseri, Temmuz 2008.
24. Kalinli, A., Geribeslemeli yapay sinir ağlarının
genetik operatörlere dayalı tabu araştırma
algoritması kullanarak eğitilmesi, Doktora
Tezi, Erciyes Üniversitesi, Elektronik
Mühendisliği Anabilim Dalı, 1996.
25. Kalinli, A., Karaboga, D., “Training recurrent
neural networks by using parallel tabu search
algorithm based on crossover operation”,
Engineering Applications of Artificial
Intelligence, Cilt 17, No 5, 529-542, 2004.
Thank you for copying data from http://www.arastirmax.com
