Comparing reliability of grid-based Quality-Diversity algorithms using artificial landscapes
This work addresses a methodological gap for researchers in the QD field, but it is incremental as it builds on existing optimization concepts without introducing new algorithms.
The authors tackled the lack of methodologies and benchmarks for comparing Quality-Diversity (QD) algorithms by proposing a simple benchmark using the Rastrigin function, an artificial landscape, to assess algorithm reliability.
Quality-Diversity (QD) algorithms are a recent type of optimisation methods that search for a collection of both diverse and high performing solutions. They can be used to effectively explore a target problem according to features defined by the user. However, the field of QD still does not possess extensive methodologies and reference benchmarks to compare these algorithms. We propose a simple benchmark to compare the reliability of QD algorithms by optimising the Rastrigin function, an artificial landscape function often used to test global optimisation methods.