Abstract
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. © 2012 Springer-Verlag Berlin Heidelberg.
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Sidiropoulos, E. (2012). Multi-objective cellular automata optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7495 LNCS, pp. 131–140). Springer Verlag. https://doi.org/10.1007/978-3-642-33350-7_14
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