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
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
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
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Using genetic algorithms to evolve behavior in cellular automata
UC'05 Proceedings of the 4th international conference on Unconventional Computation
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Evolving solutions rather than computing them certainly represents an unconventional programming approach. The general methodology of evolutionary computation has already been known in computer science since more than 40 years, but their utilization to program other algorithms is a more recent invention. In this paper, we outline the approach by giving an example where evolutionary algorithms serve to program cellular automata by designing rules for their iteration. Three different goals of the cellular automata designed by the evolutionary algorithm are outlined, and the evolutionary algorithm indeed discovers rules for the CA which solve these problems efficiently.