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Synthesis of self-replication cellular automata using genetic algorithms
Description
This paper presents an efficient searching algorithm for one-dimensional cellular automata (CAs) with self-replicating structure. In the algorithm, the CA structure is represented by a simple fitness function and a genetic algorithm is used effectively where a gene implies a rule table. Based on preliminary experimental results, we provide interesting conjectures: (1) There exists optimal mutation rate for the fitness evolution, and (2) If genes are evolved successfully, they can produce some typical patterns.
Journal
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- Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium
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Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium 173-177 vol.5, 2000-01-01
IEEE