№1, 2020

IMPLEMENTATION AND MODIFICATION OF THE SCHEME BM 25 USING GENETIC ALGORITHM

Sergey N. Lуsenko, Yuri A.Khalin

The article describes the technologies of genetic algorithms for searching information on the Internet. The amount of electronic information is growing at a tremendous pace. In such situation, the need for data retrieval and analysis systems has sharply increased, and the demand for the intellectualization of information retrieval systems has risen. Currently, there are many types of models and algorithms designed to solve different searching and processing information tasks. Each algorithm has its advantages and disadvantages. Therefore, it is important to choose the algorithm which is the most appropriate for achieving a specific purpose. This article focuses on the information concerning the modification of BM25 schemes by means of genetic algorithms. The experiment are conducted and reveal that the modification based on the genetic algorithm represents a significant improvement in an original model by obtaining more proper solutions. The results of this work allow to broaden the application field of the system and improve the accuracy and quality of information retrieval on the Internet (pp.41-48).

Keywords: genetic algorithms, mutation, adaptation, relevance, crossing.
DOI : 10.25045/jpit.v11.i1.06
References
  • Horoshko, M.B. Algoritmy, ispol'zuemye v poiskovyh sistemah: materialy VII Mezhdunar. nauch.-prakt. konf., g. Novocherkassk, 2009, 242–250 s.
  • Manning, Kristofer D. Vvedenie v informacionnyj poisk. //M: Vil'jams, 2011, 528 s.
  • Umbarkar A.J. and Sheth P.D., Crossover operators in genetic algorithms: a review. ICTACT Journalon Soft Computing, 6(1), 2015, pp. 1083–1092.
  • Arora P.K, Haleem A, Singh M.K, Kumar H. “Optimization of Cellular Manufacturing Systems using Genetic Algorithm: A Review”, Advanced Material Research Journal, 2013, vol. 622, pp. 60–63.
  • Eremeev A.V. Geneticheskie algoritmy i optimizacija uchebnoe posobie, Omsk: Izd-vo Om. gos. un-ta, 2008, 48 s.
  • Kurejchik V.V., Sorokoletov P.V., Habarova I.V. Dinamicheskie geneticheskie algoritmy v sistemah podderzhki, prinjatija reshenij, Taganrog: Izd-vo TRTU, 2006, 51 c.