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Erschienen in:
2003 | OriginalPaper | Buchkapitel
GA-Hardness Revisited
verfasst von : Haipeng Guo, William H. Hsu
Erschienen in: Genetic and Evolutionary Computation — GECCO 2003
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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Ever since the invention of Genetic Algorithms (GAs), researchers have put a lot of efforts into understanding what makes a function or problem instance hard for GAs to optimize. Many measures have been proposed to distinguish so- called GA-hard from GA-easy problems. None of these, however, has yet achieved the goal of being a reliable predictive GA-hardness measure. In this paper, we first present a general, abstract theoretical framework of instance hardness and algorithm performance based on Kolmogorov complexity. We then list several major misconceptions of GA-hardness research in the context of this theory. Finally, we propose some future directions.