Neural networks have been widely used in health monitoring applications. Genetic algorithms can also be used in similar applications as the optimization engine of the neural networks or to estimate one or more key parameters of an analytical or empirical expression(s), which represent the characteristics of the system. In this paper, capabilities, advantageous, performance and limitations of genetic algorithms and neural networks are compared on the same experimental data. Genetic algorithms are found as a promising health monitoring tool if an expression exist and necessary computational time is allowable in that particular application. Possibility of meaningful information generation and operators involvement into the decision making process are the main advantageous of this approach.
