López-Herrera, A.G.; Herrera-Viedma, E.; Herrera, F.: ¬A study of the use of multi-objective evolutionary algorithms to learn Boolean queries : a comparative study (2009)
0.00
0.0023678814 = product of:
0.0047357627 = sum of:
0.0047357627 = product of:
0.009471525 = sum of:
0.009471525 = weight(_text_:a in 1751) [ClassicSimilarity], result of:
0.009471525 = score(doc=1751,freq=8.0), product of:
0.053105544 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046056706 = queryNorm
0.17835285 = fieldWeight in 1751, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.0546875 = fieldNorm(doc=1751)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- In this article, our interest is focused on the automatic learning of Boolean queries in information retrieval systems (IRSs) by means of multi-objective evolutionary algorithms considering the classic performance criteria, precision and recall. We present a comparative study of four well-known, general-purpose, multi-objective evolutionary algorithms to learn Boolean queries in IRSs. These evolutionary algorithms are the Nondominated Sorting Genetic Algorithm (NSGA-II), the first version of the Strength Pareto Evolutionary Algorithm (SPEA), the second version of SPEA (SPEA2), and the Multi-Objective Genetic Algorithm (MOGA).
- Type
- a
