This research presents the mining of quantitative association rules based on evolutionary computation techniques.
First, a real-coded genetic algorithm that extends the well-known binary-coded CHC algorithm has been projected to determine
the intervals that define the rules without needing to discretize the attributes. The proposed algorithm is evaluated in synthetic
datasets under different levels of noise in order to test its performance and the reported results are then compared to that of
a multi-objective differential evolution algorithm, recently published. Furthermore, rules from real-world time series such as
temperature, humidity, wind speed and direction of the wind, ozone, nitrogen monoxide and sulfur dioxide have been discovered
with the objective of finding all existing relations between atmospheric pollution and climatological conditions.Ministerio de Ciencia y Tecnología TIN2007-68084-C-00Junta de Andalucía P07-TIC-0261
