GENETIC ALGORITHMS FOR LARGE-SCALE OPTIMIZATION IN CHEMOMETRICS - AN APPLICATION

The genetic algorithm is proposed as a powerful search strategy for chemometricians engaged in large-scale optimization problems. The search space is explored while past information is exploited using memory gleaned from a natural evolution process. The algorithm is robust and highly efficient at the same time. These favorable properties fit into a mathematically well-founded framework, known as the schema theorema, and render the genetic algorithm a reasonable choice for tackling complex, large-scale optimization problems. For one problem of this kind - the optimal selection of wavelengths in multi-component analysis - it is shown that the genetic algorithm is able to find acceptable solutions in a reasonable time. This feasibility study is merely the first step in a project which aims at comparing the performance of the genetic algorithm with optimization methods that have already been applied to the wavelength selection problem.