Building a Process Output Optimization Solution using Multiple Models, Ensemble Learning and a Genetic Algorithm.

The purpose of this paper is to build a Regression Model for the Concrete Strengthening Process. The description of the process and the data set can be found in the following link: http://archive.ics.uci.edu/ml/datasets/Concrete Compressive Strength This is a free and a complex dataset available from the Machine Learning Repository of Centre of Machine Learning and Intelligent Systems at University of California Irvine Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients. These ingredients include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate and age.