Sciweavers

Share
GECCO
2008
Springer

Dual-population genetic algorithm for nonstationary optimization

8 years 4 months ago
Dual-population genetic algorithm for nonstationary optimization
In order to solve nonstationary optimization problems efficiently, evolutionary algorithms need sufficient diversity to adapt to environmental changes. The dual-population genetic algorithm (DPGA) is a novel evolutionary algorithm that uses an extra population called the reserve population to provide additional diversity to the main population through crossbreeding. Preliminary experimental results on various periods and degrees of environmental change have shown that the distance between the two populations of DPGA is one of the most important factors that affect its performance. However, it is very difficult to determine the best population distance without prior knowledge about the given problem. This paper proposes a new DPGA that uses two reserve populations (DPGA2). The reserve populations are at different distances from the main population. The information inflow from the reserve populations is controlled by survival selection. Experimental results show that DPGA2 shows a bette...
Taejin Park, Ri Choe, Kwang Ryel Ryu
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2008
Where GECCO
Authors Taejin Park, Ri Choe, Kwang Ryel Ryu
Comments (0)
books