An experimental comparison of a genetic algorithm and a hill-climber for term selection

MacFarlane, A., Secker, A., May, P. & Timmis, J. (2010). An experimental comparison of a genetic algorithm and a hill-climber for term selection. Journal of Documentation, 66(4), pp. 513-531. doi: 10.1108/00220411011052939

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Abstract

Purpose – The term selection problem for selecting query terms in information filtering and routing has been investigated using hill-climbers of various kinds, largely through the Okapi experiments in the TREC series of conferences. Although these are simple deterministic approaches which examine the effect of changing the weight of one term at a time, they have been shown to improve the retrieval effectiveness of filtering queries in these TREC experiments. Hill-climbers are, however, likely to get trapped in local optima, and the use of more sophisticated local search techniques for this problem that attempt to break out of these optima are worth investigating. To this end, we apply a genetic algorithm (GA) to the same problem.

Design/Methodology/Approach – We use a standard TREC test collection from the TREC-8 filtering track, recording mean average precision and recall measures to allow comparison between the hillclimber and GA algorithms. We also vary elements of the GA, such as probability of a word being included, probability of mutation and population size in order to measure the effect of these variables. Different strategies such as Elitist and Non-Elitist methods are used, as well as Roulette Wheel and Rank selection GA algorithms.

Findings – The results of tests suggest that both techniques are, on average, better than the baseline, but the implemented GA does not match the overall performance of a hill-climber. The Rank selection algorithm does better on average than the Roulette Wheel algorithm. There is no evidence in this study that varying word inclusion probability, mutation probability or Elitist method make much difference to the overall results. Small population sizes do not appear to be as effective as larger population sizes.

Research limitations/implications – The evidence provided here would suggest that being stuck in a local optima for the term selection optimization problem does not appear to be detrimental to the overall success of the hill-climber. The evidence from term rank order would appear to provide extra useful evidence which hill-climbers can use efficiently and effectively to narrow the search space.

Originality/Value – The paper represents the first attempt to compare hill-climbers with GAs on a problem of this type.

Item Type: Article
Uncontrolled Keywords: Information searches, Programming and algorithm theory, Retrieval performance evaluation, Indexing
Subjects: Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Divisions: School of Informatics > Department of Information Science
URI: http://openaccess.city.ac.uk/id/eprint/1693

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