Cellular automata machines: a new environment for modeling
Cellular automata machines: a new environment for modeling
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Evolving cellular automata to perform computations: mechanisms and impediments
Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
An introduction to genetic algorithms
An introduction to genetic algorithms
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Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
A Representation for the Adaptive Generation of Simple Sequential Programs
Proceedings of the 1st International Conference on Genetic Algorithms
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
Mining Class Contrast Functions by Gene Expression Programming
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
From implementations to a general concept of evolvable machines
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Multi-objective optimization for dynamic single-machine scheduling
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Distance guided classification with gene expression programming
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Evolutionary algorithm based on overlapped gene expression
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part II
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Cellular automata are idealized versions of massively parallel, decentralized computing systems capable of emergent behaviors. These complex behaviors result from the simultaneous execution of simple rules at multiple local sites. A widely studied behavior consists of correctly determining the density of an initial configuration, and both human and computer-written rules have been found that perform with high efficiency at this task. However, the two best rules for the density-classification task, Coevolution1 and Coevolution2, were discovered using a coevolutionary algorithm in which a genetic algorithm evolved the rules and, therefore, only the output bits of the rules are known. However, to understand why these and other rules perform so well and how the information is transmitted throughout the cellular automata, the Boolean expressions that orchestrate this behavior must be known. The results presented in this work are a contribution in that direction.