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BASKIN GEN SEÇİMİ OPERATÖRÜNE DAYALI GENETİK ALGORİTMA MODELİ

Yıl 2011, Cilt: 26 Sayı: 4, 0 - , 20.02.2013

Öz

Bu çalışmada genetik algoritma için baskın gen seçimi operatörüne dayalı yeni bir model önerilmiştir. Önerilenmodelin performansı iyi bilinen sürekli test fonksiyonları üzerinde incelenerek sonuçlar standart genetikalgoritmaya ait sonuçlarla karşılaştırılmıştır. Elde edilen sonuçlardan önerilen yaklaşımın standart genetikalgoritmanın performansını artırdığı görülmüştür.

Kaynakça

  • Holland, J.H., Adaption in natural and artificial
  • systems, University of Michigan Press, Ann
  • Arbor, 1975.
  • 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.
  • 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,
  • -60, 1989.
  • Grefenstette, J.J., “Optimisation of control
  • parameters for genetic algorithms”, IEEE Trans.
  • SMC, Cilt 16, No 1, 122-128, 1986.
  • Reeves C.R., Rowe J.E., Genetic algorithms
  • principles and perspectives: A Guide to GA
  • Theory, ISBN 978-1402072406, Springer, 2002.
  • Michalewicz, Z., Schoenauer, M., “Evolutionary
  • algorithms for constrained parameter optimization problems”, Evolutionary
  • Computation, Cilt 4, 1-32, 1996.
  • 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.
  • Grefenstette, J.J., “Optimization of control
  • parameters for genetic algorithms”, IEEE Trans
  • on Systems, Man and Cybernetics, Cilt SMC-
  • , 122-128, 1986.
  • Schaffer, J.D., et al, “A study of control
  • parameters affecting online performance of
  • genetic algorithms for function optimization”,
  • rd International Conference on Genetic
  • Algorithms, Morgan Kaufmann, San Mateo, CA,
  • -60, 1989.
  • 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.
  • Andris, P., Frollo, I., “Optimisation of NMR coils
  • by genetic algorithms”, Measurement Science
  • Review, Cilt 2, No 2, 13-22, 2002.
  • Davidor, Y., “Analogous crossover operator”,
  • Proc 3rd Int.Conf Genetic Algorithms, George
  • Mason Univ., Arlington, VA., 98-103, 1989.
  • Fagarty, T.C., “Varing the probability of
  • mutation in the genetic algorithm”, Proc. 3rd
  • Int. Conf Genetic Algorithms, 104-109, 1989.
  • 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.
  • 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.
  • Yang, W.X., “An improved genetic algorithm
  • adopting immigration operator”, Intelligent Data
  • Analysis, Cilt 8, No 4, 385-401, 2004.
  • 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.
  • 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.
  • 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.
  • Grefenstette, J.J., “Qptimization of control
  • parameters for genetic algorithms”, IEEE Trans.
  • SMC, Cilt SMC-16, No l, 122-128, 1986.
  • 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,
  • -184, 2007.
  • Holland J.H., “Building blocks, cohort genetic
  • algorithms, and hyperplane-defined functions”,
  • Evolutionary Computation, Cilt 8, 373-391,
  • -
  • 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.
  • Kalinli, A., Geribeslemeli yapay sinir ağlarının
  • genetik operatörlere dayalı tabu araştırma
  • algoritması kullanarak eğitilmesi, Doktora
  • Tezi, Erciyes Üniversitesi, Elektronik
  • Mühendisliği Anabilim Dalı, 1996.
  • 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.
Yıl 2011, Cilt: 26 Sayı: 4, 0 - , 20.02.2013

Öz

Kaynakça

  • Holland, J.H., Adaption in natural and artificial
  • systems, University of Michigan Press, Ann
  • Arbor, 1975.
  • 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.
  • 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,
  • -60, 1989.
  • Grefenstette, J.J., “Optimisation of control
  • parameters for genetic algorithms”, IEEE Trans.
  • SMC, Cilt 16, No 1, 122-128, 1986.
  • Reeves C.R., Rowe J.E., Genetic algorithms
  • principles and perspectives: A Guide to GA
  • Theory, ISBN 978-1402072406, Springer, 2002.
  • Michalewicz, Z., Schoenauer, M., “Evolutionary
  • algorithms for constrained parameter optimization problems”, Evolutionary
  • Computation, Cilt 4, 1-32, 1996.
  • 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.
  • Grefenstette, J.J., “Optimization of control
  • parameters for genetic algorithms”, IEEE Trans
  • on Systems, Man and Cybernetics, Cilt SMC-
  • , 122-128, 1986.
  • Schaffer, J.D., et al, “A study of control
  • parameters affecting online performance of
  • genetic algorithms for function optimization”,
  • rd International Conference on Genetic
  • Algorithms, Morgan Kaufmann, San Mateo, CA,
  • -60, 1989.
  • 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.
  • Andris, P., Frollo, I., “Optimisation of NMR coils
  • by genetic algorithms”, Measurement Science
  • Review, Cilt 2, No 2, 13-22, 2002.
  • Davidor, Y., “Analogous crossover operator”,
  • Proc 3rd Int.Conf Genetic Algorithms, George
  • Mason Univ., Arlington, VA., 98-103, 1989.
  • Fagarty, T.C., “Varing the probability of
  • mutation in the genetic algorithm”, Proc. 3rd
  • Int. Conf Genetic Algorithms, 104-109, 1989.
  • 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.
  • 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.
  • Yang, W.X., “An improved genetic algorithm
  • adopting immigration operator”, Intelligent Data
  • Analysis, Cilt 8, No 4, 385-401, 2004.
  • 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.
  • 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.
  • 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.
  • Grefenstette, J.J., “Qptimization of control
  • parameters for genetic algorithms”, IEEE Trans.
  • SMC, Cilt SMC-16, No l, 122-128, 1986.
  • 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,
  • -184, 2007.
  • Holland J.H., “Building blocks, cohort genetic
  • algorithms, and hyperplane-defined functions”,
  • Evolutionary Computation, Cilt 8, 373-391,
  • -
  • 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.
  • Kalinli, A., Geribeslemeli yapay sinir ağlarının
  • genetik operatörlere dayalı tabu araştırma
  • algoritması kullanarak eğitilmesi, Doktora
  • Tezi, Erciyes Üniversitesi, Elektronik
  • Mühendisliği Anabilim Dalı, 1996.
  • 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.
Toplam 104 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Adem Kalınlı Bu kişi benim

Özgür Aksu Bu kişi benim

Yayımlanma Tarihi 20 Şubat 2013
Gönderilme Tarihi 20 Şubat 2013
Yayımlandığı Sayı Yıl 2011 Cilt: 26 Sayı: 4

Kaynak Göster

APA Kalınlı, A., & Aksu, Ö. (2013). BASKIN GEN SEÇİMİ OPERATÖRÜNE DAYALI GENETİK ALGORİTMA MODELİ. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 26(4).
AMA Kalınlı A, Aksu Ö. BASKIN GEN SEÇİMİ OPERATÖRÜNE DAYALI GENETİK ALGORİTMA MODELİ. GUMMFD. Mart 2013;26(4).
Chicago Kalınlı, Adem, ve Özgür Aksu. “BASKIN GEN SEÇİMİ OPERATÖRÜNE DAYALI GENETİK ALGORİTMA MODELİ”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 26, sy. 4 (Mart 2013).
EndNote Kalınlı A, Aksu Ö (01 Mart 2013) BASKIN GEN SEÇİMİ OPERATÖRÜNE DAYALI GENETİK ALGORİTMA MODELİ. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 26 4
IEEE A. Kalınlı ve Ö. Aksu, “BASKIN GEN SEÇİMİ OPERATÖRÜNE DAYALI GENETİK ALGORİTMA MODELİ”, GUMMFD, c. 26, sy. 4, 2013.
ISNAD Kalınlı, Adem - Aksu, Özgür. “BASKIN GEN SEÇİMİ OPERATÖRÜNE DAYALI GENETİK ALGORİTMA MODELİ”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 26/4 (Mart 2013).
JAMA Kalınlı A, Aksu Ö. BASKIN GEN SEÇİMİ OPERATÖRÜNE DAYALI GENETİK ALGORİTMA MODELİ. GUMMFD. 2013;26.
MLA Kalınlı, Adem ve Özgür Aksu. “BASKIN GEN SEÇİMİ OPERATÖRÜNE DAYALI GENETİK ALGORİTMA MODELİ”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, c. 26, sy. 4, 2013.
Vancouver Kalınlı A, Aksu Ö. BASKIN GEN SEÇİMİ OPERATÖRÜNE DAYALI GENETİK ALGORİTMA MODELİ. GUMMFD. 2013;26(4).