USING GENETIC ALGORITHMS FOR AN ARTIFICIAL NEURAL-NETWORK MODEL INVERSION

Genetic algorithms (GAs) and artificial neural networks (ANNs) are techniques for optimization and learning, respectively, which both have been adopted from nature. Their main advantage over traditional techniques is the relatively better performance when applied to complex relations. GAs and ANNs are both self-learning systems, i.e., they do not require any background knowledge from the creator. In this paper, we describe the performance of a GA that finds hypothetical physical structures of poly(ethylene terephthalate) (PET) yarns corresponding to a certain combination of mechanical and shrinkage properties. This GA uses a validated ANN that has been trained for the complex relation between structure and properties of PET. This technique was tested by comparing the optimal points found by the GA with known experimental data under a variety of multi-criteria conditions.