Abstract
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.
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Manea, F. (2005). Timed Accepting Hybrid Networks of Evolutionary Processors. In: Mira, J., Álvarez, J.R. (eds) Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach. IWINAC 2005. Lecture Notes in Computer Science, vol 3562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11499305_13
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DOI: https://doi.org/10.1007/11499305_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26319-7
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