|Diverse proposals||Reason for GA||Chromosomes||Fitness function adopted||Genetic operators used|
|(Eissa and Alghamdi, 2005) ||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.||.
|(Vallim and Coello, 2003) ||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.|
|(Li et al., 2000) ||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.|
|(Caramia et al., 2004) ||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.|
|(Rocio et al., 2008) ||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.|
|(Abe et al., 1999) ||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.|
|(Martin-Bautista et al., 1999) ||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.|
|(Marghny and Ali, 2005) ||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.|
|(Cheng et al., 1998) ||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.|
|(Lin et al., 2002) ||Improving the searching performance.||Initial population represented by binary coding selected at random.|| = ()/.
||Fitness proportion selection, Adaptive adjusting crossover, Mutation operation range.|
|(Fan et al., 2003) ||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. |
|(Milutinovic et al., 2000) ||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.|
|(Koorangi and Zamanifar, 2007) ||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.|