Complexity as Fitness for Evolved Cellular Automata Update Rules

We investigate the state change behavior of one-dimensional cellular automata during the solution of the binary density-classification task. Update rules of high, low and un- known fitness are applied to cellular au- tomata, thereby providing examples of high and low rates of successful classification. A spread factor, ω, is introduced and investi- gated as a numerical marker of state change behavior. The nature of ω describes complex or particle-like behavior on the part of the cellular automata over the middle region of initial configuration density-distribution, but breaks down at the ends. Because of the lim- itation on ω, a related jump-out term, jot, is selected for incorporation into the finess func- tion for genetic algorithm evolution of update rules. The inclusion of jot in the fitness func- tion significantly reduces the number of gen- erations required to reach high rates of suc- cessful classification (≥90%)

Complexity as Fitness for Evolved Cellular Automata Update Rules

We investigate the state change behavior of one-dimensional cellular automata during the solution of the binary density-classification task. Update rules of high, low and un- known fitness are applied to cellular au- tomata, thereby providing examples of high and low rates of successful classification. A spread factor, ω, is introduced and investi- gated as a numerical marker of state change behavior. The nature of ω describes complex or particle-like behavior on the part of the cellular automata over the middle region of initial configuration density-distribution, but breaks down at the ends. Because of the lim- itation on ω, a related jump-out term, jot, is selected for incorporation into the finess func- tion for genetic algorithm evolution of update rules. The inclusion of jot in the fitness func- tion significantly reduces the number of gen- erations required to reach high rates of suc- cessful classification (≥90%)

Complexity as Fitness for Evolved Cellular Automata Update Rules

We investigate the state change behavior of one-dimensional cellular automata during the solution of the binary density-classification task. Update rules of high, low and un- known fitness are applied to cellular au- tomata, thereby providing examples of high and low rates of successful classification. A spread factor, ω, is introduced and investi- gated as a numerical marker of state change behavior. The nature of ω describes complex or particle-like behavior on the part of the cellular automata over the middle region of initial configuration density-distribution, but breaks down at the ends. Because of the lim- itation on ω, a related jump-out term, jot, is selected for incorporation into the finess func- tion for genetic algorithm evolution of update rules. The inclusion of jot in the fitness func- tion significantly reduces the number of gen- erations required to reach high rates of suc- cessful classification (≥90%)