Enhancing Selection Hyper-heuristics via Feature Transformations
  Hyper-heuristics are a novel tool. They deal with complex optimization
problems where standalone solvers exhibit varied performance. Among such a tool
reside selection hyper-heuristics. By combining the strengths of each solver,
this kind of hyper-heuristic offers a more robust tool. However, their
effectiveness is highly dependent on the 'features' used to link them with the
problem that is being solved. Aiming at enhancing selection hyper-heuristics,
in this paper we propose two types of transformation: explicit and implicit.
The first one directly changes the distribution of critical points within the
feature domain while using a Euclidean distance to measure proximity. The
second one operates indirectly by preserving the distribution of critical
points but changing the distance metric through a kernel function. We focus on
analyzing the effect of each kind of transformation, and of their combinations.
We test our ideas in the domain of constraint satisfaction problems because of
their popularity and many practical applications. In this work, we compare the
performance of our proposals against those of previously published data.
Furthermore, we expand on previous research by increasing the number of
analyzed features. We found that, by incorporating transformations into the
model of selection hyper-heuristics, overall performance can be improved,
yielding more stable results. However, combining implicit and explicit
transformations was not as fruitful. Additionally, we ran some confirmatory
tests on the domain of knapsack problems. Again, we observed improved
stability, leading to the generation of hyper-heuristics whose profit had a
standard deviation between 20% and 30% smaller.
