Genetic algorithms in analytical chemistry

Genetic algorithms (GA's) are search algorithms that imitate nature with th eir Darwinian survival of the fittest approach. They are well suited for se arching among a large number of possibilities for solutions because they ex ploit knowledge contained in a population of initial solutions to generate new and potentially better solutions. GA's have several advantages over con ventional search techniques. First, GA's consider many points in the search space simultaneously. Because GA's utilize parallelism in which a large nu mber of candidate solutions are simultaneously searched, more of the respon se surface is probed, so there is a reduced chance of convergence to a loca l minimum. Second, genetic algorithms make no assumption about the geometry of the response surface. Hence, discontinuities or singularities in the re sponse surface, which rule out the use of derivative or simplex based metho ds, 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, c lassification, and wavelength and feature selection.