abstract = "The performance of searching agents, or
metaheuristics, like evolutionary algorithms (genetics
algorithms, genetic programming, etc.) or local search
algorithms (simulated annealing, tabu search, etc.)
depend on some properties of the search space
structure. One concept that allows us to analyse the
search space is the fitness landscape. In the case of
Genetic Programming, defining and handling fitness
landscapes is a particularly hard task, given the
complexity of the structures being evolved of the
genetic operators used. This tutorial presents some
general definitions of fitness landscape. Subsequently,
we will try to instantiate the concept of fitness
landscape to Genetic Programming, discussing problems.
The concept of landscape geometry will be introduced
and some of the most common landscape geometries and
the dynamics of Genetic Programming on those landscapes
will be discussed. After that, the binding between
fitness landscapes and problem difficulty will be
discussed and a set of measures that characterise the
difficulty of a metaheuristic in searching solutions in
a fitness landscape are analysed. Among those measures,
particular relevance will be given to Fitness Distance
Correlation (FDC), Negative Slope Coefficient (NSC), a
set of measures bound to the concept of Neutrality and
some distance metrics and/or similarity measures that
are consistent with the most commonly used genetic
operators (in particular the recently defined subtree
crossover based distance). Finally, some open questions
about fitness landscapes are discussed.",
notes = "Also known as \cite{1830916} Distributed on CD-ROM at
GECCO-2010.