Increasing the reliability and convergence of a genetic algorithm in a varying scale multi objective engineering problem

The incorporation of a traditional tripartite genetic algorithm into an engineering decision support system is discussed. In particular, the issues of achieving a reliable and accurate solution are addressed. The general design of the genetic algorithm used for this work is shown. The usual three functions of seeded selection, cross-over and mutation are applied. A fourth genetic operator, inversion, is also applied. The decision support system advises in the domain of strategic sludge disposal. The aim for the genetic algorithm is to find the optimum selection of farms where sewage sludge can be disposed of, out of all of the farms available in a region