A Probabilistic Cellular Automata Rule Forming Domino Patterns
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The objective in this study is to form a domino pattern by Cellular Automata (CA). In a previous work such patterns were formed by CA agents, which were trained with high effort by the aid of Genetic Algorithm. Now two probabilistic CA rules are designed in a methodical way that can perform this task very reliably even for rectangular fields. The first rule evolves stable sub–optimal pattern. The second rule maximizes the overlap between dominoes thereby maximizing the number of dominoes.
KeywordsPattern formation Probabilistic cellular automata Matching templates Asynchronous updating Parallel Substitution Algorithm
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