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State-of-the-art concept learning systems based on genetic algorithms evolve a redundant population of individuals, where an individual is a partial solution that covers some instances of the learning set. In this context, it is fundamental that the population be diverse and that as many instances as possible be covered. The universal suffrage selection (US) operator is a powerful selection mechanism that addresses these two requirements. In this paper we compare experimentally the US operator with two variants, called Weighted US (WUS) and Exponentially Weighted US (EWUS), of this operator in the system ECL [1].

Non-universal Suffrage Selection Operators Favor Population Diversity in Genetic Algorithms