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Diverse proposals | Reason for GA | Chromosomes | Fitness function adopted | Genetic operators used |
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(Eissa and Alghamdi, 2005) [30] | Genetic algorithm is used to optimize the profiles whereas the relevance feedback is used to adapt it. | Represent a gene as a term, an individual as a document and the population as the profile. | .
| Selection. |
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(Vallim and Coello, 2003) [31] | Combines user’s feedback to new documents retrieved by the agent with a genetic algorithm. | Individuals represented by a query vector and its adaptation rate. | .
| Two point crossover and Mutation operator. |
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(Li et al., 2000) [32] | Realize the scheduling strategy of agent manager. | Search space is represented as weight field in the search engine. Field are search parameters. | Adaptation function (agent) = .
| One point Cross over and Single point Mutation. |
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(Caramia et al., 2004) [33] | Select a subset of original pages for which the sum of scores is large. | Chromosomes represent subsets of pages of bounded cardinality. Each page is a gene. | .
| Single point crossover. |
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(Rocio et al., 2008) [34] | Evolving lofty quality query. | Chromosome is represented as a list of terms where each term corresponds to a gene. | Fitness (q) = max() .
| Roulette Wheel Selection, Single point crossover, One point mutation. |
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(Abe et al., 1999) [35] | For evolving information retrieval agents. | Genes are represented by the search parameters. | (SH/MH + SI/MI) (1 − ST/TL) + (1 − ME/MM). | Selection uses ranking strategy, Uniform crossover, and Single Point Mutation. |
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(Martin-Bautista et al., 1999) [36] | Adaptive internet information retrieval. | Each gene represents a fuzzy subset of the document set by means of a Keyword term and number of occurrences in a document. | where j and j is the pay off of life tax and chromosome number respectively. | Random selection, Double point Crossover, Random Mutation. |
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(Marghny and Ali, 2005) [19] | Steady state genetic algorithm for optimizing web search. | Initial population is generated by heuristic creation operator which queries standard engines to obtain pages. | Fitness function evaluates web pages is a mathematical formulation of Link quality, Page quality and Mean quality function. | Binary tournament selection, Single point crossover. |
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(Cheng et al., 1998) [22] | GA implemented as a spider to find most relevant home pages in the entire internet. | Chromosomes represent all input home pages in a set. | Jaccard’s coefficient function. | Heuristic based cross over, Simple mutation. |
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(Lin et al., 2002) [20] | Improving the searching performance. | Initial population represented by binary coding selected at random. | = ()/.
| Fitness proportion selection, Adaptive adjusting crossover, Mutation operation range. |
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(Fan et al., 2003) [37] | Genetic Programming to the ranking function discovery problem leveraging the structural information of HTML documents. | Chromosomes represent html pages. | The fitness evaluation of each ranking tree is done at the level multiple queries. , .
| Single point Crossover, One point Mutation. |
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(Milutinovic et al., 2000) [38] | Genetic search algorithms enable intelligent and efficient internet searches. | Chromosomes represent set of input Web sites given by a user. | Jaccard’s Function. | Topic Mutation, Spatial Mutation, Temporal Mutation. |
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(Koorangi and Zamanifar, 2007) [14] | Query reformulation in search engine. | Initial population consists of first five keywords of the user dictionary. | CHK fitness function. | One point crossover, Inversion mutation operator. |
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