Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Small worlds: the dynamics of networks between order and randomness
Small worlds: the dynamics of networks between order and randomness
Evolutionary computation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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
Linked
Cellular Automata Rule Detection Using Circular Asynchronous Evolutionary Search
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Electronic Notes in Theoretical Computer Science (ENTCS)
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Cellular Automata (CAs) represent useful and important tools in the study of complex systems and interactions. The problem of finding CA rules able to generate a desired global behavior is considered of great importance and highly challenging. Evolutionary computing offers promising models for addressing this inverse problem of global to local mapping. A related approach less investigated refers to finding robust network topologies that can be used in connection with a simple fixed rule in CA computation. The focus of this study is the evolution and dynamics of small-world networks for the density classification task in CAs. The best evolved networks are analyzed in terms of their tolerance to dynamic network changes. Results indicate a good performance and robustness of the obtained small-world networks for CA density problem.