Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
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
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Using a genetic algorithm to evolve cellular automata for 2D/3D computational development
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Fitness landscape of the cellular automata majority problem: View from the "Olympus"
Theoretical Computer Science
Self-adaptive mutation rates in genetic algorithm for inverse design of cellular automata
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A Neuro-Genetic Framework for Pattern Recognition in Complex Systems
Fundamenta Informaticae - Membrane Computing
Using genetic algorithms to evolve behavior in cellular automata
UC'05 Proceedings of the 4th international conference on Unconventional Computation
A Neuro-Genetic Framework for Pattern Recognition in Complex Systems
Fundamenta Informaticae - Membrane Computing
Dynamic Fault-Tolerant three-dimensional cellular genetic algorithms
Journal of Parallel and Distributed Computing
| Hi-index | 0.00 |
Cellular automata are used in many fields to generate a global behavior with local rules. Finding the rules that display a desired behavior can be a hard task especially in real world problems. This paper proposes an improved approach to generate these transition rules for multi dimensional cellular automata using a genetic algorithm, thus giving a generic way to evolve global behavior with local rules, thereby mimicking nature. Three different problems are solved using multi dimensional topologies of cellular automata to show robustness, flexibility and potential. The results suggest that using multiple dimensions makes it easier to evolve desired behavior and that combining genetic algorithms with multi dimensional cellular automata is a very powerful way to evolve very diverse behavior and has great potential for real world problems.