Improving genetic search for one-dimensional cellular automata, using heuristics related to their dynamic behavior forecast

As part of the comprehensive theme of the relationships between dynamic systems and computational theories, a very active area of research has been the relationships between the generic dynamic behavior of Cellular Automata (CA) and their computational abilities. Various investigations have been carried out on the computational power of CA, with concentrated efforts in the study of one-dimensional CA and their computational abilities. One of the approaches is the use of Genetic Algorithms (GA) to look for CA with a predefined computational behavior. A set of parameters which we have previously shown to be effective in helping forecast CA dynamic behavior, are used here as an auxiliary metric to guide the GA search. To this end, we modified selection, mutation and crossover of a GA, so as to incorporate the heuristic, and obtained very effective results in the evolution search for CA that can solve the so-called synchronization task