Comparing reliability of grid-based Quality-Diversity algorithms using artificial landscapes

07/23/2019
by   Leo Cazenille, et al.
0

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset