5. A computer-implemented method, comprising: generating a population of candidate rules for detecting duplicates, each duplicate representing different item description information entries that describe a common item, wherein each candidate rule comprises one or more independent rule conditions and one or more elements for each independent rule condition specifying how a portion of one set of item description information should be compared to another set of item description information for duplicate detection; for each candidate rule of the population, applying that rule to a reference data set including known duplicates and non-duplicates in order to determine a measure of that candidate rule's performance with respect to detecting duplicates; assigning each candidate rule a fitness score based on the respective performance measurement for that candidate rule, wherein the fitness score of a given candidate rule is representative of that candidate rule's probability of being selected as a parent for a new generation of candidate rules; based on the fitness scores, selecting a subset of the population of candidate rules as parents for the new generation of candidate rules; changing one or more elements of one or more rule conditions of one or more of the parent candidate rules to generate the new generation of candidate rules; and selecting from the new generation of candidate rules or one or more subsequent generations of candidate rules, one or more rules for inclusion within a rule set for detecting duplicates within item description information.
7. The method of claim 5 , wherein for each candidate rule of the population, determining the measure of that candidate rule's performance comprises applying that rule to the reference data set including known duplicates and non-duplicates in order to determine measures of precision and recall for that candidate rule.