Int J Neural Syst - An optimization spiking neural p system for approximately solving combinatorial optimization problems.

Tópicos

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Resumo

Membrane systems (also called P systems) refer to the computing models abstracted from the structure and the functioning of the living cell as well as from the cooperation of cells in tissues, organs, and other populations of cells. Spiking neural P systems (SNPS) are a class of distributed and parallel computing models that incorporate the idea of spiking neurons into P systems. To attain the solution of optimization problems, P systems are used to properly organize evolutionary operators of heuristic approaches, which are named as membrane-inspired evolutionary algorithms (MIEAs). This paper proposes a novel way to design a P system for directly obtaining the approximate solutions of combinatorial optimization problems without the aid of evolutionary operators like in the case of MIEAs. To this aim, an extended spiking neural P system (ESNPS) has been proposed by introducing the probabilistic selection of evolution rules and multi-neurons output and a family of ESNPS, called optimization spiking neural P system (OSNPS), are further designed through introducing a guider to adaptively adjust rule probabilities to approximately solve combinatorial optimization problems. Extensive experiments on knapsack problems have been reported to experimentally prove the viability and effectiveness of the proposed neural system.

Resumo Limpo

membran system also call p system refer comput model abstract structur function live cell well cooper cell tissu organ popul cell spike neural p system snps class distribut parallel comput model incorpor idea spike neuron p system attain solut optim problem p system use proper organ evolutionari oper heurist approach name membraneinspir evolutionari algorithm miea paper propos novel way design p system direct obtain approxim solut combinatori optim problem without aid evolutionari oper like case miea aim extend spike neural p system esnp propos introduc probabilist select evolut rule multineuron output famili esnp call optim spike neural p system osnp design introduc guider adapt adjust rule probabl approxim solv combinatori optim problem extens experi knapsack problem report experiment prove viabil effect propos neural system

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