Towards Landscape Analysis in Adaptive Learning of Surrogate Models

Abstrakt

A context in which we expect adaptive learning to be promising is the choice of a suitable optimization strategy in black-box optimization. The reason why strategy adaptation is needed in such a situation is that knowledge of the blackbox objective function is obtained only gradually during the optimization. That knowledge covers two aspects: 1. the landscape of the black-box objective, revealed through its evaluation in previous iterations./ 2. success or failure of the optimization strategies applied to that black-box objective in previous iterations. To extract landscape knowledge, landscape analysis has been developed during the last decade. To include also the second aspect, we complement features obtained using the landscape analysis with features describing the optimization employed in previous iterations. Our interest is in expensive black-box optimization, where the number of evaluations of the expensive objective is usually decreased using a suitable surrogate model. Therefore, the research reported in this extended abstract addresses adaptive learning of surrogate models, more precisely their learning in surrogateassisted versions of the state-of-the-art black-box optimization method, Covariance Matrix Adaptation Evolution Strategy (CMA-ES).