Representação ternária em algoritmos evolutivos para a obtenção de autômatos celulares binários

Abstract

The search for cellular automata (CAs) rules capable of executing a determined task can be impossible to be achieve manually, given the huge size of the search space usually involved. A method for being able to find rules capable of executing the task at hand has been the usage of genetic algorithms (GAs) to evolve an initially random population, until the desired objective; each individual of those GAs are usually represented by a candidate transition rule. In problems formulated for the resolution by a binary cellular automaton, a recently used modification on the representation of the individuals was the usage of templates with the presence of an extra symbol, capable of representing every other valid symbol. Such schema of ternary representation is used on the present work, aiming for it s net effect on the search process on the GAs. This study is made for two classical tasks of binary unidimensional cellular automata, the density classification task and the parity problem, both traditional in the context of using GAs for searching transition rules with high performance. By comparing the results of the original GAs and their versions implemented with the ternary representation, it s shown that the ternary representation is able to improve the quality of the results. Particularly, it is analized the condition in which the usage of the ternary representation presents to be more effective, as well as some effects of the actual implementation. Possible future works are presented at the end.