Abstract
One well-known disadvantage of evolutionary algorithms (EAs) for rule discovery is that in general they are slow, by comparison with rule discovery algorithms based on the rule induction paradigm. After all, rule induction algorithms usually perform a kind of local search in the rule space, whereas EAs are population-based algorithms that perform a more global search of the rule space.
“In a world where serial algorithms are usually made parallel through countless tricks and contortions, it is no small irony that genetic algorithms (highly parallel algorithms) are made serial through equally unnatural tricks and turns.”
[Goldberg 1989, p. 208]
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Freitas, A.A. (2002). Scaling up Evolutionary Algorithms for Large Data Sets. In: Data Mining and Knowledge Discovery with Evolutionary Algorithms. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04923-5_11
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