Shade mutation strategy analysis via dynamic simulation in complex network

This paper presents a novel approach to visualizing Evolutionary Algorithm (EA) dynamic in complex network and analyses the greediness of "current-topbest/1" mutation strategy used in state-of-art Differential Evolution (DE) algorithm-Success-History based Adaptive DE (SHADE) on CEC2015 benchmark set of test functions. Provided analysis suggests that the greediness might not be the optimal approach for guiding the evolution. © ECMS Zita Zoltay Paprika, Péter Horák, Kata Váradi,Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, János Péter Rádics (Editors).Grant Agency of the Czech Republic - GACR [P103/15/06700S]; Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme [LO1303, MSMT-7778/2014]; European Regional Development Fund under the Project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2017/004