Geodesic
The Model of Interaction Between Learning and Evolutionary Optimization
Matematičeskaâ biologiâ i bioinformatika, Tome 7 (2012), pp. 676-691.

Voir la notice de l'article provenant de la source Math-Net.Ru

The model of interaction between learning and evolutionary optimization is designed and investigated. The evolving population of modeled organisms is considered. The mechanism of genetic assimilation of the acquired features during number of generations of Darwinian evolution is studded. The genetic assimilation means that individually acquired features are “re-invented” by evolution and recorded directly into the genome of organisms. It is shown that genetic assimilation takes place as follows: the organism distribution moves towards the optimum at learning and further selection; then the genomes of selected organisms also move towards the optimum. The mechanism of influence of the learning load is analyzed. It is shown that the learning load leads to a significant acceleration of evolution. The hiding effect is also studded; this effect means that a strong learning inhibits the evolutionary search in some situations. 
@article{MBB_2012_7_a16,
     author = {Vladimir G. Red'ko},
     title = {The {Model} of {Interaction} {Between} {Learning} and {Evolutionary} {Optimization}},
     journal = {Matemati\v{c}eska\^a biologi\^a i bioinformatika},
     pages = {676--691},
     publisher = {mathdoc},
     volume = {7},
     year = {2012},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/MBB_2012_7_a16/}
}
TY  - JOUR
AU  - Vladimir G. Red'ko
TI  - The Model of Interaction Between Learning and Evolutionary Optimization
JO  - Matematičeskaâ biologiâ i bioinformatika
PY  - 2012
SP  - 676
EP  - 691
VL  - 7
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/MBB_2012_7_a16/
LA  - ru
ID  - MBB_2012_7_a16
ER  - 
%0 Journal Article
%A Vladimir G. Red'ko
%T The Model of Interaction Between Learning and Evolutionary Optimization
%J Matematičeskaâ biologiâ i bioinformatika
%D 2012
%P 676-691
%V 7
%I mathdoc
%U http://geodesic.mathdoc.fr/item/MBB_2012_7_a16/
%G ru
%F MBB_2012_7_a16
Vladimir G. Red'ko. The Model of Interaction Between Learning and Evolutionary Optimization. Matematičeskaâ biologiâ i bioinformatika, Tome 7 (2012), pp. 676-691. http://geodesic.mathdoc.fr/item/MBB_2012_7_a16/

[1] Baldwin J. M., “A new factor in evolution”, American Naturalist, 30 (1896), 441–451 <ext-link ext-link-type='doi' href='https://doi.org/10.1086/276408'>10.1086/276408</ext-link>

[2] Morgan C. L., “On modification and variation”, Science, 4 (1896), 733–740 <ext-link ext-link-type='doi' href='https://doi.org/10.1126/science.4.99.733'>10.1126/science.4.99.733</ext-link>

[3] Osborn H. F., “Ontogenetic and phylogenetic variation”, Science, 4 (1896), 786–789 <ext-link ext-link-type='doi' href='https://doi.org/10.1126/science.4.100.786'>10.1126/science.4.100.786</ext-link>

[4] Waddington C. H., “Canalization of development and inheritance of acquired characters”, Nature, 150 (1942), 563–565 <ext-link ext-link-type='doi' href='https://doi.org/10.1038/150563a0'>10.1038/150563a0</ext-link>

[5] Belew R. K., Mitchell M. (eds.), Adaptive Individuals in Evolving Populations: Models and Algorithms, Addison-Wesley, Massachusetts, 1996

[6] Turney P., Whitley D., Anderson R. (eds.), “Evolution, Learning, and Instinct: 100 Years of the Baldwin Effect”, Special Issue of Evolutionary Computation on the Baldwin Effect, 4:3 (1996)

[7] Hinton G. E., Nowlan S. J., “How learning can guide evolution”, Complex Systems, 1 (1987), 495–502 <ext-link ext-link-type='zbl-item-id' href='https://zbmath.org/?q=an:0651.92015'>0651.92015</ext-link>

[8] Mayley G., “Guiding or hiding: Explorations into the effects of learning on the rate of evolution”, ECAL 97, Proceedings of the Fourth European Conference on Artificial Life, eds. Husbands P., Harvey I., MIT Press, Cambridge, Massachusetts, 1997, 135–144

[9] Ackley D., Littman M., “Interactions between learning and evolution”, Artificial Life II, Proceedings of the Second Artificial Life Workshop, eds. Langton C. G., Taylor C., Farmer J. D., Rasmussen S., Addison-Wesley, Redwood City CA, 1992, 487–509

[10] Red'ko V. G., Mosalov O. P., Prokhorov D. V., “A model of evolution and learning”, Neural Networks, 18:5–6 (2005), 738–745 <ext-link ext-link-type='doi' href='https://doi.org/10.1016/j.neunet.2005.06.005'>10.1016/j.neunet.2005.06.005</ext-link>

[11] Redko V. G., Redko O. V., “Bionicheskaya model geneticheskoi assimilyatsii priobretaemykh navykov”, Nauchnaya sessiya NIYaU MIFI-2010. XII Vserossiiskaya nauchno-tekhnicheskaya konferentsiya «Neiroinformatika-2010», Sbornik nauchnykh trudov, V 2-kh chastyakh, v. 1, NIYaU MIFI, M., 2010, 191–198

[12] Eigen M., Samoorganizatsiya materii i evolyutsiya biologicheskikh makromolekul, Mir, M., 1973

[13] Eigen M., Shuster P., Gipertsikl. Printsipy samoorganizatsii makromolekul, Mir, M., 1982

[14] Redko V. G., Evolyutsiya, neironnye seti, intellekt. Modeli i kontseptsii evolyutsionnoi kibernetiki, URSS, M., 2005

[15] Redko V. G., Tsoi Yu. R., “Otsenka effektivnosti evolyutsionnykh algoritmov”, Doklady AN, 404:3 (2005), 312–315 <ext-link ext-link-type='mr-item-id' href='http://mathscinet.ams.org/mathscinet-getitem?mr=2216820'>2216820</ext-link>

[16] Kimura M., Molekulyarnaya evolyutsiya: teoriya neitralnosti, Mir, M., 1985

[17] Redko V. G., Tsoi Yu. R., “Otsenka skorosti i effektivnosti evolyutsionnykh algoritmov”, Bionicheskie informatsionnye sistemy i ikh prakticheskie primeneniya, eds. Zinchenko L. A., Kureichika V. M., Redko V. G., Fizmatlit, M., 2011, 109–126

[18] Redko V. G., “Spinovye stekla i evolyutsiya”, Biofizika, 35:5 (1990), 831–834