Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A Machine-Independent Theory of the Complexity of Recursive Functions
Journal of the ACM (JACM)
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Membrane Computing: An Introduction
Membrane Computing: An Introduction
Solving NP-Complete Problems With Networks of Evolutionary Processors
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Accepting networks of splicing processors: Complexity results
Theoretical Computer Science
Information Processing Letters
The many facets of natural computing
Communications of the ACM
On Accepting Networks of Evolutionary Processors with at Most Two Types of Nodes
LATA '09 Proceedings of the 3rd International Conference on Language and Automata Theory and Applications
Theoretical Computer Science
On the power of networks of evolutionary processors
MCU'07 Proceedings of the 5th international conference on Machines, computations, and universality
The role of evolutionary operations in accepting hybrid networks of evolutionary processors
Information and Computation
Accepting hybrid networks of evolutionary processors
DNA'04 Proceedings of the 10th international conference on DNA computing
Accepting networks of splicing processors
CiE'05 Proceedings of the First international conference on Computability in Europe: new Computational Paradigms
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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We propose a computational model that is inspired by genetic operations over strings such as mutation and crossover. The model, Accepting Network of Genetic Processors, is highly related to previously proposed ones such as Networks of Evolutionary Processors and Networks of Splicing Processors. These models are complete computational models inspired by DNA evolution and recombination. Here, we prove that the proposed model is computationally complete (it is equivalent to the Turing machine). Hence, it can accept any recursively enumerable language. In addition, we relate the proposed model with (parallel) Genetic Algorithms or Evolutionary Programs and we set these techniques as decision problem solvers.