DSpace Collection:
http://hdl.handle.net/11368/2829352
2019-08-24T02:52:44ZTissue P systems can be simulated efficiently with counting oracles
http://hdl.handle.net/11368/2947976
Title: Tissue P systems can be simulated efficiently with counting oracles
Abstract: We prove that polynomial-time tissue P systems with cell division or cell separation can be simulated efficiently by Turing machines with oracles for counting problems. This shows that the corresponding complexity classes are included in ^#, thus improving, under standard complexity theory assumptions, the previously known upper bound .2015-01-01T00:00:00ZPivotal seeding for K-means based on clustering ensembles
http://hdl.handle.net/11368/2946994
Title: Pivotal seeding for K-means based on clustering ensembles
Abstract: Despite its large use, one major limitation of K-means algorithm is the impact of the initial seeding on the final partition. We propose a modified version, using the information contained in a co-association matrix obtained from clustering ensembles; such matrix is given as input for a set of pivotal methods, implemented in the pivmet R package, used to perform a pivot-based initialization step. Preliminary results concerning the comparison with the classical approach and other clustering methods are discussed.2019-01-01T00:00:00ZElectricity demand modelling with genetic programming
http://hdl.handle.net/11368/2947974
Title: Electricity demand modelling with genetic programming
Abstract: Load forecasting is a critical task for all the operations of power systems. Especially during hot seasons, the influence of weather on energy demand may be strong, principally due to the use of air conditioning and refrigeration. This paper investigates the application of Genetic Programming on day-ahead load forecasting, comparing it with Neural Networks, Neural Networks Ensembles and Model Trees. All the experimentations have been performed on real data collected from the Italian electric grid during the summer period. Results show the suitability of Genetic Programming in providing good solutions to this problem. The advantage of using Genetic Programming, with respect to the other methods, is its ability to produce solutions that explain data in an intuitively meaningful way and that could be easily interpreted by a human being. This fact allows the practitioner to gain a better understanding of the problem under exam and to analyze the interactions between the features that characterize it.2015-01-01T00:00:00ZComplexity classes for membrane systems: A survey
http://hdl.handle.net/11368/2947972
Title: Complexity classes for membrane systems: A survey
Abstract: The computational power of membrane systems, in their different variants, can be studied by defining classes of problems that can be solved within given bounds on computation time or space, and comparing them with usual computational complexity classes related to the Turing Machine model. Here we will consider in particular membrane systems with active membranes (where new membranes can be created by division of existing membranes). The problems related to the definition of time/space complexity classes for membrane systems will be discussed, and the resulting hierarchy will be compared with the usual hierarchy of complexity classes, mainly through simulations of Turing Machines by (uniform families of) membrane systems with active membranes.2015-01-01T00:00:00Z