3,041 research outputs found
Computational Aspects of Asynchronous CA
This work studies some aspects of the computational power of fully
asynchronous cellular automata (ACA). We deal with some notions of simulation
between ACA and Turing Machines. In particular, we characterize the updating
sequences specifying which are "universal", i.e., allowing a (specific family
of) ACA to simulate any TM on any input. We also consider the computational
cost of such simulations
Balanced crossover operators in Genetic Algorithms
In several combinatorial optimization problems arising in cryptography and design theory, the admissible solutions must often satisfy a balancedness constraint, such as being represented by bitstrings with a fixed number of ones. For this reason, several works in the literature tackling these optimization problems with Genetic Algorithms (GA) introduced new balanced crossover operators which ensure that the offspring has the same balancedness characteristics of the parents. However, the use of such operators has never been thoroughly motivated, except for some generic considerations about search space reduction. In this paper, we undertake a rigorous statistical investigation on the effect of balanced and unbalanced crossover operators against three optimization problems from the area of cryptography and coding theory: nonlinear balanced Boolean functions, binary Orthogonal Arrays (OA) and bent functions. In particular, we consider three different balanced crossover operators (each with two variants: \u201cleft-to-right\u201d and \u201cshuffled\u201d), two of which have never been published before, and compare their performances with classic one-point crossover. We are able to confirm that the balanced crossover operators perform better than one-point crossover. Furthermore, in two out of three crossovers, the \u201cleft-to-right\u201d version performs better than the \u201cshuffled\u201d version
Special Issue: Generative Models in Artificial Intelligence and Their Applications
Castelli, M. (Guest ed.), & Manzoni, L. (Guest ed.) (2022). Special Issue: Generative Models in Artificial Intelligence and Their Applications. Applied Sciences (Switzerland), 12(9), [4127]. https://doi.org/10.3390/app12094127In recent years, artificial intelligence has been used to generate a significant amount of high-quality data, such as images, music, and videos. The creation of such a vast amount of synthetic data was made possible due to the improved performance of different machine learning techniques, such as artificial neural networks. Considering the increased interest in this area, new techniques for automatic data generation and augmentation have recently been proposed. For instance, generative adversarial networks (GANs) and their variants are nowadays popular techniques in this research field. The creation of synthetic data was also achieved with evolutionary-based techniques, for instance, in the context of multimedia artifacts creationpublishersversionpublishe
Characterizing PSPACE with Shallow Non-Confluent P Systems
In P systems with active membranes, the question of understanding the
power of non-confluence within a polynomial time bound is still an open problem. It is
known that, for shallow P systems, that is, with only one level of nesting, non-con
uence
allows them to solve conjecturally harder problems than con
uent P systems, thus reaching PSPACE. Here we show that PSPACE is not only a bound, but actually an exact
characterization. Therefore, the power endowed by non-con
uence to shallow P systems
is equal to the power gained by con
uent P systems when non-elementary membrane
division and polynomial depth are allowed, thus suggesting a connection between the
roles of non-confluence and nesting depth
Characterizing PSPACE with Shallow Non-Confluent P Systems
In P systems with active membranes, the question of understanding the
power of non-confluence within a polynomial time bound is still an open problem. It is
known that, for shallow P systems, that is, with only one level of nesting, non-con
uence
allows them to solve conjecturally harder problems than con
uent P systems, thus reaching PSPACE. Here we show that PSPACE is not only a bound, but actually an exact
characterization. Therefore, the power endowed by non-con
uence to shallow P systems
is equal to the power gained by con
uent P systems when non-elementary membrane
division and polynomial depth are allowed, thus suggesting a connection between the
roles of non-confluence and nesting depth
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