Adaptive non-uniform, hyper-elitist evolutionary method for the optimization of plasmonic biosensors

Biosensors are analytical devices which aim is to determine the concentration of biological substances. Biosensors represent a rapidly expanding interdisciplinary field with numerous transducers which are based principally on potentiometric, calorimetric, amperometric, colorimetric and optical, measurements. The complexity of physical phenomenon involved in the optical (or plasmonic) sensors is linked to the use of metallic nanostructured materials, in a resonant mode, which governs its sensitivity. The aim of optimization is both to increase the efficiency of the sensor, and to adjust the thickness of the costly materials used in its fabrication (gold). The optimization of such sensors implies the detection of poles in complex plane, from a model involving a few of transcendental functions of the parameters. An heuristic method is therefore proposed and compared to the classical Schwefel's scheme, with application to SPR biosensors. The crossover algorithm uses the quality of elements as weight and is therefore hyper elitist. A nonuniform mutation with adaption of its bounds on the topology of the fitness function facilitates the detection of resonance and accelerates the convergence. Both modifications of the evolutionary algorithm decrease the number of generations necessary to reach convergence of one order of magnitude. The optimized gold thickness is found to be different from that usually used for the SPR biosensor, especially when the increase of the sensitivity of biosensor is of interest.