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Perry, R.; Willett, P.: ¬A revies of the use of inverted files for best match searching in information retrieval systems (1983) 0.01

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Source: Journal of information science. 6(1983), S.59-66

Robertson, A.M.; Willett, P.: Use of genetic algorithms in information retrieval (1995) 0.00
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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
Jones, G.; Robertson, A.M.; Willett, P.: ¬An introduction to genetic algorithms and to their use in information retrieval (1994) 0.00
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Abstract

This paper provides an introduction to genetic algorithms, a new approach to the investigation of computationally-intensive problems that may be insoluble using conventional, deterministic approaches. A genetic algorithm takes an initial set of possible starting solutions and then iteratively improves theses solutions using operators that are analogous to those involved in Darwinian evolution. The approach is illusrated by reference to several problems in information retrieval

Willett, P.: Best-match text retrieval (1993) 0.00

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Source: Library and information briefings. 1993, no.49, S.1-11

Robertson, M.; Willett, P.: ¬An upperbound to the performance of ranked output searching : optimal weighting of query terms using a genetic algorithms (1996) 0.00

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Abstract: Describes the development of a genetic algorithm (GA) for the assignment of weights to query terms in a ranked output document retrieval system. The GA involves a fitness function that is based on full relevance information, and the rankings resulting from the use of these weights are compared with the Robertson-Sparck Jones F4 retrospective relevance weight

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