The effects of fitness functions on genetic
programming-based ranking discovery for web search
Journal of the American Society for Information
Science and Technology, 55 (7):
628--636 (2004)DOI: doi:10.1002/asi.20009
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 task- discovery 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 well 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 on 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 on the design
of fitness functions for genetic-based information
retrieval experiments.
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