A genetic algorithm for the routing of droplets in DMFB: Preliminary results

Digital Microfluidic Biochips (DMFB) are revolutionizing clinical diagnostics and most of the biochemical processes that take place in a laboratory, mainly because of its capability for automation, low cost, portability, and efficiency. One important challenge in designing such devices is the droplet routing problem, which basically consists in mobilizing each droplet in the DMFB from its source to its target cells so that fluidic and timing constraints are met, while minimizing simultaneously the amount of cells used and the arrival time of the last droplet. In this paper, we propose a novel representation along with the corresponding genetic operators for the NSGA-II to deal with the droplet routing problem, where the number of used cells and the last droplet arrival time are optimized. Experimental results show that our representation and genetic operators lead to competitive results with respect to those obtained by state-of-the-art methods. To the best of the authors knowledge, this is the first time the droplet routing problem is dealt with an evolutionary algorithm approach.