A Learning Classifier System for Three-Dimensional Shape Optimization

A learning classifier system complex is developed in order to accomplish the broader goal of developing a methodology to perform generalized zeroth-order two- and three-dimensional shape optimization. Specifically, the methodology has the objective of determining the optimal boundary to minimize mass while satisfying constraints on stress and geometry. Even with the enormous advances in shape optimization no method has proven to be satisfactory across the broad spectrum of optimization problems facing the modern engineer. Similarly the available software in the field of learning classifier systems is so embryonic that a new software package had to be developed for this application. The shape optimization via hypothesizing inductive classifier system complex (SPHINcsX) instantiates the methodology in a software package overcoming many of the limitations of today's conventional shape optimization techniques, while advancing the state-of-the-art in classifier system software tools.