Multi-objective Cellular Automata Optimization

摘要

The role of cellular automata in optimization is a current area of research. This paper presents a multi-objective approach to cellular optimization. A typical nonlinear problem of spatial resource allocation is treated by two alternative methods. The first one is based on a specially designed operative genetic algorithm and the second one on a hybrid annealing - genetic procedure. Pareto front approximations are computed by the two methods and also by a non-cellular version of the second approach. The better performance of the cellular methods is demonstrated and questions for further research are discussed.