Abstract
In the pertinent literature, an ongoing discussion can be found about whether evolutionary algorithms are better suited for optimization or adaptation. Unfortunately, the pertinent literature does not offer a definition of the difference between adaptation and optimization. As a working hypothesis, this paper proposes adaptation as tracking the moving optimum of a dynamically changing fitness function as opposed to optimization as finding the optimum of a static fitness function. The results presented in this paper suggest that providing enough variation among the population members and applying a selection scheme is sufficient for adaptation. The resulting performance, however, depends on the problem, the selection scheme, the variation operators, as well as possibly other factors.
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Salomon, R., Eggenberger, P. (1998). Adaptation on the evolutionary time scale: A working hypothesis and basic experiments. In: Hao, JK., Lutton, E., Ronald, E., Schoenauer, M., Snyers, D. (eds) Artificial Evolution. AE 1997. Lecture Notes in Computer Science, vol 1363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0026605
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DOI: https://doi.org/10.1007/BFb0026605
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