DISH solving the GNBG-generated test suite

Abstract

This paper presents an extended abstract describing an entry into the benchmarking competition on a new GNBG-generated Test Suite. We are presenting the results of our previously published Distance based parameter adaptation for Success-History based Differential Evolution (DISH) algorithm based on state of the art adaptive differential evolution variants. The key feature of our algorithm is a prolonged exploration due to an updated weighting scheme for control parameter adaptation. The results show that our contestant is able to locate the global optima on 12 out of the 24 test functions.