Evolving two-dimensional cellular automata to perform density classification: A report on work in progress

Task‐performing dynamics in irregular, biomimetic networks

“…[In examining different inputs depending on the current node state, this rule is akin to the Gacs-Kurdyumov-Levin (GKL) rule [16] for the majority task in 1D. It differs substantially, however, from the so-called "2DGKL" rule discussed in Jimenez-Morales et al [17]. Because of interchangeability of state values, as well as NE/NW and SW/SE inputs, there are actually four identically performing 2DGKL rules.]…”

Applying genetic algorithms for calibrating a hexagonal cellular automata model for the simulation of debris flows characterised by strong inertial effects

“…Recently, GA have also been used in order to improve the performance of CA for resolving computationally difficult tasks. Jiménez-Morales et al (2001) applied GA to explore the CA brules' spaceQ in order to obtain nontrivial bcollective behaviourQ in CA. Tomassini and Venzi (2002) evolved asynchronous CA to face similar problems through genetic algorithms; GA have also been applied to CA for modelling bioremediation of contaminated soils by Di Gregorio et al (1996Gregorio et al ( , 1997.…”

Control of Stationary Behavior in Probabilistic Boolean Networks by Means of Structural Intervention

“…When the numbers of genes and predictors per gene get large, the advantages of GAs should become dramatically more apparent. For example, in [7], GAs were used to design two-dimensional cellular automata for a density classification task. In that work, the search space consisted of 2 512 states -a hyper-astronomical number that is not even remotely enumerable.…”