Shape irregularity of Life-Like Network Automaton rules as an indicator of classification performance
The study introduced 'jaggedness,' a metric quantifying the visual irregularity of Life-Like Network Automaton (LLNA) rules. This parameter measures how much the rule's shape deviates from regularity. The research found that higher jaggedness correlates with better network classification performance, suggesting rule shape is a key factor in determining network dynamics.