Fifty years of research on self-replication: an overview
Artificial Life - Special issue on self-replication
Self-replicating structures: evolution, emergence, and computation
Artificial Life - Special issue on self-replication
String Rewriting Grammar Optimized Using an Evolvability Measure
ECAL '01 Proceedings of the 6th European Conference on Advances in Artificial Life
Evolution in asynchronous cellular automata
ICAL 2003 Proceedings of the eighth international conference on Artificial life
IEEE Intelligent Systems
Genetic approaches to search for computing patterns in cellular automata
IEEE Computational Intelligence Magazine
Natural Computing: an international journal
Synthesis of desired binary cellular automata through the genetic algorithm
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Extensions and variations on construction of autoreplicators in typogenetics
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
Evolutionary discovery of arbitrary self-replicating structures
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part II
Self-replication, evolvability and asynchronicity in stochastic worlds
SAGA'05 Proceedings of the Third international conference on StochasticAlgorithms: foundations and applications
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Previous computational models of self-replication using cellular automata (CA) have been manually designed, a difficult and time-consuming process. We show here how genetic algorithms can be applied to automatically discover rules governing self-replicating structures. The main difficulty in this problem lies in the choice of the fitness evaluation technique. The solution we present is based on a multiobjective fitness function consisting of three independent measures: growth in number of components, relative positioning of components, and the multiplicity of replicants. We introduce a new paradigm for CA models with weak rotational symmetry, called orientation-insensitive input, and hypothesize that it facilitates discovery of self-replicating structures by reducing search-space sizes. Experimental yields of self-replicating structures discovered using our technique are shown to be statistically significant. The discovered self-replicating structures compare favorably in terms of simplicity with those generated manually in the past, but differ in unexpected ways. These results suggest that further exploration in the space of possible self-replicating structures will yield additional new structures. Furthermore, this research sheds light on the process of creating self-replicating structures, opening the door to future studies on the discovery of novel self-replicating molecules and self-replicating assemblers in nanotechnology