Abstract
Much attention has been paid in the GA literature to understand, characterize or to predict the notion of difficulty for genetic algorithms. Formal or informal ways of handling difficulty in previous work are commented. This points out a major problem of scaling especially when comparing differently distributed fitness functions.
Hamming fitness functions are proposed as a basis to scale difficulty measures, and to account for GA parameters bias. The use of a basis relaxes the dependence on fitness scale or distribution. Different measures are also proposed to characterize GA behavior, distinguishing convergence time and on-line GA radial and effective trajectory distance in Hamming space.
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Kallel, L. (1998). Inside GA dynamics: Ground basis for comparison. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN V. PPSN 1998. Lecture Notes in Computer Science, vol 1498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056849
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DOI: https://doi.org/10.1007/BFb0056849
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