Searching for one-dimensional cellular automata in the absence of a priori information

Various investigations have been carried out on the computational power of cellular automata (CA), with concentrated efforts in the study of one-dimensional CA. One of the approaches is the use of genetic algorithms (GA) to look for CA with a predefined computational behavior. We have previously shown a set of parameters that can be effective in helping forecast CA dynamic behavior; here, they are used as an heuristic to guide the GA search, by biasing selection, mutation and crossover, in the context of the Grouping Task (GT) for one-dimensional CA. Since GT is a new task, no a priori knowledge about its solutions is presently available; even then, the incorporation of the parameter-based heuristic entails a significant improvement over the results achieved by the plain genetic search.