Search (2 results, page 1 of 1)

0.012480095 = product of:
  0.02496019 = sum of:
    0.02496019 = product of:
      0.04992038 = sum of:
        0.04992038 = weight(_text_:22 in 1484) [ClassicSimilarity], result of:
          0.04992038 = score(doc=1484,freq=2.0), product of:
            0.16128273 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046056706 = queryNorm
            0.30952093 = fieldWeight in 1484, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=1484)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

13. 9.2014 14:45:22

0.001353075 = product of:
  0.00270615 = sum of:
    0.00270615 = product of:
      0.0054123 = sum of:
        0.0054123 = weight(_text_:a in 2418) [ClassicSimilarity], result of:
          0.0054123 = score(doc=2418,freq=2.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.10191591 = fieldWeight in 2418, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0625 = fieldNorm(doc=2418)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Reviews the basic techniques involving genetic algorithms and their application to 2 problems in information retrieval: the generation of equifrequent groups of index terms; and the identification of optimal query and term weights. The algorithm developed for the generation of equifrequent groupings proved to be effective in operation, achieving results comparable with those obtained using a good deterministic algorithm. The algorithm developed for the identification of optimal query and term weighting involves fitness function that is based on full relevance information