@article{101_b0a4bd11-a8c6-4980-875a-9f7caf882815,

title = {Efficiency of Genetic Algorithm For Subject Search Queries},

author = {Ivanov V.K. and Palyukh B.V. and Sotnikov A.N.},

url = {https://disk.yandex.ru/i/DWS1H4M7CMLXxQ},

doi = {10.1134/S1995080216030124},

issn = {1995-0802},

year  = {2016},

date = {2016-09-29},

urldate = {2025-01-21},

journal = {Lobachevskii Journal of Mathematics},

volume = {37},

number = {3},

pages = {244–254},

publisher = {Pleiades Publishing, Ltd.},

abstract = {<p>The article presents and generalizes the results on some performance indicators of genetic algorithm developed by authors and applied to effective search queries and selection of relevant results after document subject search. It is shown that the developed technology expands opportunities of semantic search and increases the number of the found relevant results. In particular, we made an effort to show the ability of the developed algorithm to achieve the neighborhood of the fitness function in a finite number of steps, to provide higher precision of search in comparison with the well-known search engines of the Internet as well as to provide the acceptable semantic relevance of the found documents.</p>},

note = {Ivanov V.K., Palyukh B.V., Sotnikov A.N. Efficiency of Genetic Algorithm For Subject Search Queries. Lobachevskii Journal of Mathematics, 2016, Vol. 37, No. 3, pp. 244–254. Pleiades Publishing, Ltd., 2016.},

keywords = {convergence, data centre, fitness function, genetic algorithm, innovation index, population, ranking, relevance, search precision, search query},

pubstate = {published},

tppubtype = {article}

}