  Model-based evolutionary algorithms (EAs) adapt an underlying search model to
features of the problem at hand, such as the linkage between problem variables.
The performance of EAs often deteriorates as multiple modes in the fitness
landscape are modelled with a unimodal search model. The number of modes is
however often unknown a priori, especially in a black-box setting, which
complicates adaptation of the search model. In this work, we focus on models
that can adapt to the multi-modality of the fitness landscape. Specifically, we
introduce Hill-Valley Clustering, a remarkably simple approach to adaptively
cluster the search space in niches, such that a single mode resides in each
niche. In each of the located niches, a core search algorithm is initialized to
optimize that niche. Combined with an EA and a restart scheme, the resulting
Hill-Valley EA (HillVallEA) is compared to current state-of-the-art niching
methods on a standard benchmark suite for multi-modal optimization. Numerical
results in terms of the detected number of global optima show that, in spite of
its simplicity, HillVallEA is competitive within the limited budget of the
benchmark suite, and shows superior performance in the long run.
