When combined with machine learning, the black-box analysis of fitness landscapes promises to provide us with easy-to-compute features that can be used to select and configure an algorithm that is well-suited to the task at hand. As applications that involve computationally expensive, stochastic simulations become increasingly relevant in practice, however, there is a need for landscape features that are both (A) possible to estimate with a very limited budget of fitness evaluations, and (B) accurate in the presence of small to moderate amounts of noise. We show via a small set of relatively inexpensive landscape features based on hill-climbing methods that these two goals are in tension with each other: cheap features are sometimes extremely sensitive to even very small amounts of noise. We propose that features whose values are calculated using population-based search methods may provide a path forward in developing landscape analysis tools that are both inexpensive and robust to noise.
CITATION STYLE
Scott, E. O., & De Jong, K. A. (2016). Landscape features for computationally expensive evaluation functions: Revisiting the problem of noise. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9921 LNCS, pp. 952–961). Springer Verlag. https://doi.org/10.1007/978-3-319-45823-6_89
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