Evolving Sequential Combinations of Elementary Cellular Automata Rules

• Published in: Advances in Artificial Life pp. 461 - 470
• Main Authors: Martins, Claudio L. M., de Oliveira, Pedro P. B.
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Cellular Automaton; Computer science; control theory; systems; Density Classification; Entire Lattice; Exact sciences and technology; Regular Expression; Sequential Combination; Artificial life; Cellular automaton; Cellular computation; Genetic algorithm; Character string; Evolutionary algorithm; Classification; Artificial intelligence; Algorithm analysis
• ISBN: 9783540288480; 3540288481
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11553090_47

Performing computations with cellular automata, individually or arranged in space or time, opens up new conceptual issues in emergent, artificial life type forms of computation, and opens up the possibility of novel technological advances. Here, a methodology for combining sequences of elementary cellular automata is presented, in order to perform a given computation. The problem at study is the well-known density classification task that consists of determining the most frequent bit in a binary string. The methodology relies on an evolutionary algorithm, together with analyses driven by background knowledge on dynamical behaviour of the rules and their parametric estimates, as well as those associated with the formal behaviour characterisation of the rules involved. The resulting methodology builds upon a previous approach available in the literature, and shows its efficacy by leading to 2 rule combinations already known, and to additional 26, apparently unknown so far.