Using genetic algorithms to find suboptimal retrieval expert combinations

Authors:
Holger Billhardt;Daniel Borrajo;Víctor Maojo
Affiliations:
Univ. Rey Juan Carlos, 28933 Móstoles (Madrid), Spain;Univ. Carlos III de Madrid, 28911 Leganés (Madrid), Spain;Univ. Politécnica de Madrid, 28660 Boadilla del Monte, (Madrid), Spain
Venue:
Proceedings of the 2002 ACM symposium on Applied computing
Year:
2002

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Abstract

A common problem of expert combination approaches in Information Retrieval (IR) is the selection of both, the experts to be combined and the combination function. In most studies the experts are selected from a rather small set of candidates using some heuristics. Thus, only a reduced number of possible combinations is considered and other possibly better solutions are left out. In this paper we propose the use of genetic algorithms to find a suboptimal combination of experts for a document collection. Our system automatically determines both, the experts to be combined and the parameters of the combination function. We test and evaluate the approach on four classical text collections. The results show that the learnt combination strategies perform better than any of the individual methods and that genetic algorithms provide a viable method to learn expert combinations.