Genetic algorithms (GA's) are search algorithms that imitate nature with their Darwinian survival of the fittest approach. They are well suited for searching among a large number of possibilities for solutions because they exploit knowledge contained in a population of initial solutions to generate new and potentially better solutions. GA's have several advantages over conventional search techniques. First, GA's consider many points in the search space simultaneously. Because GA's utilize parallelism in which a large number of candidate solutions are simultaneously searched, more of the response surface is probed, so there is a reduced chance of convergence to a local minimum. Second, genetic algorithms make no assumption about the geometry of the response surface. Hence, discontinuities or singularities in the response surface, which rule out the use of derivative or simplex based methods, will not pose a problem for GA's. Third, the computational environment offered by a GA can be readily adjusted to match a particular application. Thus, GA's can be tailored for individual problems. Consequently, GA's can be used to solve a variety of data analysis problems in chemistry including curve fitting, parameter estimation, function optimization, calibration, classification, and wavelength and feature selection.