Introduction to algorithms
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
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
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
Hi-index | 0.00 |
Accepting Hybrid Networks of Evolutionary Processors are bio-inspired, massively parallel computing models that have been used succesfully in characterizing several usual complexity classes and also in solving efficiently decision problems. However, this model does not seem close to the usual algorithms, used in practice, since, in general, it lacks the property of stopping on every input. We add new features in order to construct a model that has this property, and also, is able to characterize uniformly CoNP, issue that was not solved in the framework of regular AHNEPs. This new model is called Timed AHNEPs (TAHNEP). We continue by adressing the topic of problem solving by means of this new defined model. Finally, we propose a tehnique that can be used in the design of algorithms as efficient as possible for a given problem; this tehnique consists in defining the notion of Problem Solver, a model that extends the previously defined TAHNEP.