The Network of polarizedevolutionaryprocessors (NPEP) is a rather new variant of the bio-inspired computing model called Network of evolutionaryprocessors (NEP). This model, together with its variants, is able to p...
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ISBN:
(纸本)9781450372534
The Network of polarizedevolutionaryprocessors (NPEP) is a rather new variant of the bio-inspired computing model called Network of evolutionaryprocessors (NEP). This model, together with its variants, is able to provide theoretical feasible solutions to hard computational problems. NPEPE is a software engine able to simulate NPEP which is deployed over Giraph, an ultra-scalable platform based on the Bulk Synchronous Parallel (BSP) programming model. Rather surprisingly, the BSP model and the underlying architecture of NPEP have many common points. Moreover, these similarities are also shared with all variants in the NEP family. We take advantage of these similarities and propose an extension of NPEPE (named gNEP) that enhances it to simulate any variant of the NEP's family. Our extended gNEP framework, presents a twofold contribution. Firstly, a flexible architecture able to extend software components in order to include other NEP models (including the seminal NEP model and new ones). Secondly, a component able to translate input configuration files representing the instance of a problem and an algorithm based on different variants of the NEP model into some suitable input files for gNEP framework. In this work, we simulate a solution to the "3-colorability" problem which is based on NPEP. We compare the results for a specific experiment using NPEPE engine and gNEP. Moreover, we show several experiments in the aim of studying, in a preliminary way, the scalability offered by gNEP to easily deploy and execute instances of problems requiring more intensive computations.
In this paper we consider a new variant of networks of polarized evolutionary processors (NPEP) named Generalized networks of evolutionarypolarizedprocessors (GNPEP) and propose them as solvers of combinatorial opti...
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In this paper we consider a new variant of networks of polarized evolutionary processors (NPEP) named Generalized networks of evolutionarypolarizedprocessors (GNPEP) and propose them as solvers of combinatorial optimization problems. Unlike the NPEP model, GNPEP uses its numerical evaluation over the processed data from a quantitative perspective, hence this model might be more suitable to solve specific hard problems in a more efficient and economic way. In particular, we propose a GNPEP network to solve a well-known NP-hard problem, namely the N-queens. We prove that this GNPEP algorithm requires a linear time in the size of a given instance. This result suggests that the GNPEP model is more suitable to address problems related to combinatorial optimization in which integer restrictions have a relevant role.
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