Genetic Algorithm Forecasting for Telecommunications Products

In this paper, we describe genetic algorithms (GA’s) for forecasting long-term quarterly sales of products in the telecommunications technology sector using widely available economic indicators such as Disposable Personal Income and New Housing Starts as independent variables. Individual chromosomes indicated inclusion or disinclusion of specific economic variables, as well as operational rules for combining the variables. Population evolution utilized random crossover mating, mutation, and inversion. Several features beyond those of the canonical GA were also incorporated, including evolution of individuals in distinct ecosystems with a specified level of intermarriage between ecosystems, the capability for a single gene in an individual’s chromosome to indicate a subroutine call to the complete chromosome of an individual from a previous generation, and hill-climbing applied to improve the most fit offspring produced by a generation. At a forecast interval of eight quarters, individuals exhibiting maximal fitness achieved RMS forecast errors below the the average two-week sales figure.