Computing-Inspired Detection of Multiple Cancers

A new computing-inspired multiple-cancer detection procedure (MCDP) is proposed. In the MCDP, the cancer areas to be detected can be regarded as solutions of an objective function, the tissue region around the cancer areas can be mapped to the parameter space of the solutions, and the nanorobots correspond to the agents in the optimization procedure. The process that the nanorobots look for the cancer areas by swimming in the tissue region can be mapped to the process that the agents search for the solutions in the parameter space. Niche Genetic Algorithm (NGA) is widely used in multimodal function optimization and non-monotonic function optimization. It can search all global optimums of multiple hump function in a running, keep the diversity of the population effectively, and avoid premature of solutions got from normal GA. Inspired by the optimization procedure of NGA, the multiple cancer detection procedure (MCDP) has been studied and the NGA-inspired cancer detection procedure has been proposed in order to locate the targets efficiently at the same time by taking into account realistic in vivo propagation and controlling of nanorobots. Finally, some comparative numerical examples are presented to demonstrate the effectiveness of the NGA-inspired MCDP.