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Volume 7   Issue 2   Year 2012
References

  1. Baldwin JM. A new factor in evolution. American Naturalist. 1896;30:441-451. doi: 10.1086/276408
  2. Morgan CL. On modification and variation. Science. 1896;4:733-740. doi: 10.1126/science.4.99.733
  3. Osborn HF. Ontogenetic and phylogenetic variation. Science. 1896;4:786-789. doi: 10.1126/science.4.100.786
  4. Waddington CH. Canalization of development and inheritance of acquired characters. Nature. 1942;150:563-565. doi: 10.1038/150563a0
  5. Adaptive Individuals in Evolving Populations: Models and Algorithms. Eds. Belew RK and Mitchell M. Massachusetts: Addison-Wesley; 1996.
  6. Evolution, Learning, and Instinct: 100 Years of the Baldwin Effect: Special Issue of Evolutionary Computation on the Baldwin Effect. Eds. Turney P, Whitley D, Anderson R. 1996;4(3).
  7. Hinton GE, Nowlan SJ. How learning can guide evolution. Complex Systems. 1987;1:495-502.
  8. Mayley G. Guiding or hiding: Explorations into the effects of learning on the rate of evolution. In: Proceedings of the Fourth European Conference on Artificial Life (ECAL 97). Eds. Husbands P and Harvey I. Cambridge, Massachusetts: MIT Press; 1997. P. 135-144.
  9. Ackley D, Littman M. Interactions between learning and evolution. In: Artificial Life II: Proceedings of the Second Artificial Life Workshop. Eds. Langton CG, Taylor C, Farmer JD, Rasmussen S. Redwood City CA: Addison-Wesley; 1992. P. 487-509.
  10. Red’ko VG, Mosalov OP, Prokhorov DV. A model of evolution and learning. Neural Networks. 2005;18(5-6):738-745. doi: 10.1016/j.neunet.2005.06.005
  11. Red'ko VG, Red'ko OV. In: Nauchnaia sessiia NIIaU MIFI - 2010. XII Vserossiiskaia nauchno-tekhnicheskaia konferentsiia «Neiroinformatika-2010»: sbornik nauchnykh trudov. (Scientific Session of the Moscow Engineering Physics institute (National Research Nuclear University) – 2010. The XIIth All-Russian Scientific and Technical Conference “Neuroinformatics – 2010”). Moscow; 2010. P. 191-198 (in Russ.).
  12. Eigen M. Molekulare Selbstorganisation und Evolution (Self-Organization of Matter and the Evolution of Biological Macromolecules). Naturwissenschaften Bd. 1971;58(10):465-523. doi: 10.1007/BF00623322
  13. Eigen M, Schuster P. The Hypercycle - A Principle of Natural Self-Organization. Springer-Verlag, Berlin; 1979.
  14. Red'ko VG. Evoliutsiia, neironnye seti, intellekt. Modeli i kontseptsii evoliutsionnoi kibernetiki (Evolution, Neural Networks, Intelligence. Models and Conceptions of Evolutionary Cybernetics). Moscow; 2005 (in Russ.).
  15. Red'ko VG, Tsoy YuR. Estimation of the efficiency of evolution algorithms. Doklady Mathematics. 2005;72(2):810-813.
  16. Kimura M. The Neutral Theory of Molecular Evolution. Cambridge, Cambridge University Press; 1983. doi: 10.1017/CBO9780511623486
  17. Red'ko VG, Tsoy YuR. In: Bionicheskie informatsionnye sistemy i ikh prakticheskie primeneniia (Bionic Information Systems and their Applications). Eds. Kureichik V, Red’ko V, Zinchenko L. Moscow; 2011. P.110-127 (in Russ.).
  18. Red'ko VG. Biofizika (Biofizika). 1990;35(5):831-834.
Table of Contents Original Article
Red’ko V.G. The Model of Interaction Between Learning and Evolutionary Optimization. Ìàthematical biology and bioinformatics. 2012;7(2):676-691. doi: 10.17537/2012.7.676
(published in Russian)

Abstract (rus.)
Abstract (eng.)
Full text (rus., pdf)
References Translation into English
Red’ko V.G. The Model of Interaction Between Learning and Evolutionary Optimization. Ìàthematical biology and bioinformatics. 2014;9(Suppl.):t1-t15. doi: 10.17537/2014.9.t1

Full text (eng., pdf)

 

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