Membrane computing is an emergent branch of natural computing, which is inspired by the structure and the functioning of living cells, as well as the organization of cells in tissues, organs, and other higher order st...Membrane computing is an emergent branch of natural computing, which is inspired by the structure and the functioning of living cells, as well as the organization of cells in tissues, organs, and other higher order structures. Tissue P systems are a class of the most investigated computing mod- els in the framework of membrane computing, especially in the aspect of efficiency. To generate an exponential resource in a polynomial time, cell separation is incorporated into such systems, thus obtaining so called tissue P systems with cell separation. In this work, we exploit the computational efficiency of this model and construct a uniform family of such tissue P systems for solving the independent set problem, a well-known NP-complete problem, by which an efficient so- lution can be obtained in polynomial time.展开更多
The paper discusses an enhancement to a recently presented supervised learning algorithm to solve the Maximum Independent Set problem.In particular,it is shown that the algorithm can be improved by simplifying the tas...The paper discusses an enhancement to a recently presented supervised learning algorithm to solve the Maximum Independent Set problem.In particular,it is shown that the algorithm can be improved by simplifying the task learnt by the neural network adopted,with measurable effects on the quality of the solutions provided on unseen instances.Empirical results are presented to validate the idea..展开更多
文摘Membrane computing is an emergent branch of natural computing, which is inspired by the structure and the functioning of living cells, as well as the organization of cells in tissues, organs, and other higher order structures. Tissue P systems are a class of the most investigated computing mod- els in the framework of membrane computing, especially in the aspect of efficiency. To generate an exponential resource in a polynomial time, cell separation is incorporated into such systems, thus obtaining so called tissue P systems with cell separation. In this work, we exploit the computational efficiency of this model and construct a uniform family of such tissue P systems for solving the independent set problem, a well-known NP-complete problem, by which an efficient so- lution can be obtained in polynomial time.
基金supported by the Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung(CH)(No.200020-182360)。
文摘The paper discusses an enhancement to a recently presented supervised learning algorithm to solve the Maximum Independent Set problem.In particular,it is shown that the algorithm can be improved by simplifying the task learnt by the neural network adopted,with measurable effects on the quality of the solutions provided on unseen instances.Empirical results are presented to validate the idea..
