1. A computer-implemented method, comprising: generating a population of candidate rules for detecting duplicates, each duplicate representing different sets of item description information that describe a common item, wherein each candidate rule comprises multiple 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 measures of precision and recall for that candidate rule; assigning each candidate rule a fitness score generated with a fitness function based on the measures of precision and recall determined 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; performing one or more operations on the parent candidate rules to generate the new generation of candidate rules, said operations including one or more of: a crossover operation for generating a new candidate rule including different rule conditions from two different parent candidate rules, and a mutation operation for randomly or pseudo-randomly modifying a rule condition passed from a parent candidate rule to a candidate rule of the new generation, wherein modifying a rule condition comprises changing one or more elements of the rule condition; 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.