Fan, W.; Fox, E.A.; Pathak, P.; Wu, H.: ¬The effects of fitness functions an genetic programming-based ranking discovery for Web search (2004)
0.01
0.006995496 = product of:
0.03147973 = sum of:
0.015556021 = weight(_text_:of in 2239) [ClassicSimilarity], result of:
0.015556021 = score(doc=2239,freq=12.0), product of:
0.061262865 = queryWeight, product of:
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.03917671 = queryNorm
0.25392252 = fieldWeight in 2239, product of:
3.4641016 = tf(freq=12.0), with freq of:
12.0 = termFreq=12.0
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.046875 = fieldNorm(doc=2239)
0.015923709 = product of:
0.031847417 = sum of:
0.031847417 = weight(_text_:22 in 2239) [ClassicSimilarity], result of:
0.031847417 = score(doc=2239,freq=2.0), product of:
0.13719016 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.03917671 = queryNorm
0.23214069 = fieldWeight in 2239, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.046875 = fieldNorm(doc=2239)
0.5 = coord(1/2)
0.22222222 = coord(2/9)
- Abstract
- Genetic-based evolutionary learning algorithms, such as genetic algorithms (GAs) and genetic programming (GP), have been applied to information retrieval (IR) since the 1980s. Recently, GP has been applied to a new IR taskdiscovery of ranking functions for Web search-and has achieved very promising results. However, in our prior research, only one fitness function has been used for GP-based learning. It is unclear how other fitness functions may affect ranking function discovery for Web search, especially since it is weIl known that choosing a proper fitness function is very important for the effectiveness and efficiency of evolutionary algorithms. In this article, we report our experience in contrasting different fitness function designs an GP-based learning using a very large Web corpus. Our results indicate that the design of fitness functions is instrumental in performance improvement. We also give recommendations an the design of fitness functions for genetic-based information retrieval experiments.
- Date
- 31. 5.2004 19:22:06
- Source
- Journal of the American Society for Information Science and technology. 55(2004) no.7, S.628-636