2010
DOI: 10.1016/j.datak.2009.10.010
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Structure of morphologically expanded queries: A genetic algorithm approach

Abstract: a b s t r a c tIn this paper we deal with two issues. First, we discuss the negative effects of term correlation in query expansion algorithms, and we propose a novel and simple method (query clauses) to represent expanded queries which may alleviate some of these negative effects. Second, we discuss a method to optimize local query-expansion methods using genetic algorithms, and we apply this method to improve stemming. We evaluate this method with the novel query representation method and show very significa… Show more

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Cited by 19 publications
(4 citation statements)
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“…(1) GAs work on a population of points instead of a single point; (2) GAs use only the values of the objective function not their derivatives or other auxiliary knowledge; (3) GAs use probabilistic transition functions and not deterministic ones [15].…”
Section: Introductionmentioning
confidence: 99%
“…(1) GAs work on a population of points instead of a single point; (2) GAs use only the values of the objective function not their derivatives or other auxiliary knowledge; (3) GAs use probabilistic transition functions and not deterministic ones [15].…”
Section: Introductionmentioning
confidence: 99%
“…GA is one of popular and successful computational models in the field of intelligent computing [24], especially for dealing with NP-hard problems. Along with other intelligent computing techniques such as fuzzy computing, neural networks and multi-agent systems, GAs develop more and more strongly and are widely applied in different fields [25,26]. Our GA design takes into account conflicting elements in PPI networks in order to reduce unnecessary edges, thus greatly improves computing speed.…”
Section: Accepted Manuscriptmentioning
confidence: 99%
“…The process of selection, crossover and mutation continues for a fixed number of generations or until a termination condition is met [11]. GAs differ from classical optimization techniques such as the gradient-based algorithm in the following ways: 1) GAs make use of the encoding of the parameters not the parameters themselves; 2) GAs work on a population of points as opposed to a single point; 3) GAs use only the values of the objective function not their derivatives or other auxiliary knowledge; 4) GAs use probabilistic transition functions and not deterministic ones [12].…”
Section: Introduction Of Gamentioning
confidence: 99%