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
On the size complexity of hybrid networks of evolutionary processors
Theoretical Computer Science - Descriptional complexity of formal systems
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Hybrid networks of evolutionary processors
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Solving 3CNF-SAT and HPP in linear time using WWW
MCU'04 Proceedings of the 4th international conference on Machines, Computations, and Universality
Accepting hybrid networks of evolutionary processors
DNA'04 Proceedings of the 10th international conference on DNA computing
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In this paper we approach the problem of constructing an accepting device for the class of context-free languages, based on a newly defined computational model: Accepting Hybrid Network of Evolutionary Processors (AHNEPs). Although it is known that AHNEPs are Turing complete, and, consequently, for every context-free language, seen as a recursively enumerable language, we can construct an AHNEP that simulates the Turing machine accepting it, we choose a direct approach: the AHNEPs we design simulate the computation done by a non-deterministic push-down automata accepting the language. This approach leads to a more economic AHNEP since the number of processors we use depends linearly on the number of states and the number of working symbols of the automaton, while for the network obtained in the general case of recursively enumerable languages, the number of processors is linear in the number of states, but quadratic in the number of working symbols of a Turing machine accepting the given language. Finally, we particularize the AHNEP architecture we proposed in order to recognize regular languages. We obtain an upper bound for the number of processors needed close to that known for generating hybrid networks of evolutionary processors.