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...

• Type: Conference
• Year: 2008
• Where: GECCO
• Authors: Taejin Park, Ri Choe, Kwang Ryel Ryu
• Keywords: Evolutionary Algorithms, GECCO 2008, Nonstationary Optimization Problems, Optimization, Reserve Populations