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This paper presents a new method to generalize strategies in order to control parameters of Evolutionary Algorithms (EAs). A learning process establishes the relationship between optimal quality parameters and diversity, and simplifies control to just one variable, highly correlated with Exploration/Exploitation Balance, in such way that strategies can be defined in more abstract terms. The acquired knowledge is expressed in a simple fashion that helps the user to understand internal mechanicsdoi:10.1109/cec.2007.4425067 dblp:conf/cec/MaturanaS07 fatcat:ovkn5d6ehffvpdvvpx3jgsn7le