Memristive network-based genetic algorithm and its application to image edge detection
Yongbin Yu, Chenyu Yang, Deng Quanxin, Nyima Tashi, Liang Shouyi, Zhou Chen
Abstract
This paper proposes a mem-computing model of memristive network-based genetic algorithm (MNGA) by building up the relationship between the memristive network (MN) and the genetic algorithm (GA), and a new edge detection algorithm where image pixels are defined as individuals of population. First, the computing model of MNGA is designed to perform mem-computing, which brings new possibility of the hardware implementation of GA. Secondly, MNGA-based edge detection integrating image filter and GA operator deployed by MN is proposed. Finally, simulation results demonstrate that the figure of merit (FoM) of our model is better than the latest memris-tor-based swarm intelligence. In summary, a new way is found to build proper matching of memristor to GA and aid image edge detection.