Abstract
The structure of the fitness landscape on which
genetic programming operates is examined. The
landscapes of a range of problems of known difficulty
are analyzed in an attempt to determine which landscape
measures correlate with the difficulty of the problem.
The autocorrelation of the fitness values of random
walks, a measure which has been shown to be related to
perceived difficulty using other techniques, is only a
weak indicator of the difficulty as perceived by
genetic programming. All of these problems show
unusually low autocorrelation. Comparison of the range
of landscape basin depths at the end of adaptive walks
on the landscapes shows good correlation with problem
difficulty, over the entire range of problems
examined.
- (artificial
- adaptive
- algorithm
- algorithms,
- autocorrelation,
- basin
- depths,
- fitness
- genetic
- intelligence),
- landscape
- landscapes,
- learning
- measures,
- problems,
- programming,
- random
- search
- theory,
- walks
- walks,
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