Abstract
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.
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© 2003 Springer-Verlag Berlin Heidelberg
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Guo, H., Hsu, W.H. (2003). GA-Hardness Revisited. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45110-2_36
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DOI: https://doi.org/10.1007/3-540-45110-2_36
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