Generation of simple structured Information Retrieval functions by genetic algorithm without stagnation
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- @Article{Kulunchakov:2017:ESA,
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author = "Kulunchakov A. S. and Strijov V. V.",
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title = "Generation of simple structured Information Retrieval
functions by genetic algorithm without stagnation",
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journal = "Expert Systems with Applications",
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year = "2017",
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ISSN = "0957-4174",
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DOI = "doi:10.1016/j.eswa.2017.05.019",
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URL = "http://www.sciencedirect.com/science/article/pii/S0957417417303354",
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abstract = "This paper investigates an approach to construct new
ranking models for Information Retrieval. The IR
ranking model depends on the document description. It
includes the term frequency and document frequency. The
model ranks documents upon a user request. The quality
of the model is defined by the difference between the
documents, which experts assess as relative to the
request, and the ranked ones. To boost the model
quality a modified genetic algorithm was developed. It
generates models as superpositions of primitive
functions and selects the best according to the quality
criterion. The main impact of the research if the new
technique to avoid stagnation and to control structural
complexity of the consequently generated models. To
solve problems of stagnation and complexity, a new
criterion of model selection was introduced. It uses
structural metric and penalty functions, which are
defined in space of generated superpositions. To show
that the newly discovered models outperform the other
state-of-the-art IR scoring models the authors perform
a computational experiment on TREC datasets. It shows
that the resulted algorithm is significantly faster
than the exhaustive one. It constructs better ranking
models according to the MAP criterion. The obtained
models are much simpler than the models, which were
constructed with alternative approaches. The proposed
technique is significant for developing the information
retrieval systems based on expert assessments of the
query-document relevance.",
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keywords = "genetic algorithms, genetic programming, Information
retrieval, Ranking function, Evolutionary stagnation,
Overfitting",
- }
Genetic Programming entries for
Andrey Kulunchakov
Vadim Strijov
Citations