Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Evolution of Asynchronous Cellular Automata for the Density Task
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A Genetic Algorithm Discovers Particle-Based Computation in Cellular Automata
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Dynamics of networks evolved for cellular automata computation
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
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A circular evolutionary model is proposed to produce Cellular Automata (CA) rules for the computationally emergent task of density classification. The task refers to determining the initial density most present in the initial cellular state of a one-dimensional cellular automaton within a number of update steps. This is a challenging problem extensively studied due to its simplicity and potential to generate a variety of complex behaviors. The proposed circular evolutionary model aims to facilitate a good exploitation of relevant genetic material while increasing the population diversity. This goal is achieved by integrating a fitness guided population topology with an asynchronous search scheme. Both selection and recombination take place asynchronously enabling a gradual propagation of information from the fittest individuals towards the less fit members of the population. Numerical experiments emphasize a competitive performance of the circular search algorithm compared to other evolutionary models indicating the potential of the proposed model.