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

Editorial: Sweet sixteen

This is the traditional triennial note used by the editors to give the readers of 4OR information on the state of the journal and its future. In the 3years that have passed since the last editorial note (Liberti et al. in Q J Oper 13:1–13, 2015), three volumes (each containing four issues) of the journal have been published: vol. 13 (2015), vol. 14 (2016), and vol. 15 (2017)

A tutorial for competent memetic algorithms: Model, taxonomy and design issues

The combination of evolutionary algorithms with local search was named "memetic algorithms" (MAs) (Moscato, 1989). These methods are inspired by models of natural systems that combine the evolutionary adaptation of a population with individual learning within the lifetimes of its members. Additionally, MAs are inspired by Richard Dawkin's concept of a meme, which represents a unit of cultural evolution that can exhibit local refinement (Dawkins, 1976). In the case of MA's, "memes" refer to the strategies (e.g., local refinement, perturbation, or constructive methods, etc.) that are employed to improve individuals. In this paper, we review some works on the application of MAs to well-known combinatorial optimization problems, and place them in a framework defined by a general syntactic model. This model provides us with a classification scheme based on a computable index D, which facilitates algorithmic comparisons and suggests areas for future research. Also, by having an abstract model for this class of metaheuristics, it is possible to explore their design space and better understand their behavior from a theoretical standpoint. We illustrate the theoretical and practical relevance of this model and taxonomy for MAs in the context of a discussion of important design issues that must be addressed to produce effective and efficient MAs

Genetic algorithms with guided and local search strategies for university course timetabling

This article is posted here with permission from the IEEE - Copyright @ 2011 IEEEThe university course timetabling problem (UCTP) is a combinatorial optimization problem, in which a set of events has to be scheduled into time slots and located into suitable rooms. The design of course timetables for academic institutions is a very difficult task because it is an NP-hard problem. This paper investigates genetic algorithms (GAs) with a guided search strategy and local search (LS) techniques for the UCTP. The guided search strategy is used to create offspring into the population based on a data structure that stores information extracted from good individuals of previous generations. The LS techniques use their exploitive search ability to improve the search efficiency of the proposed GAs and the quality of individuals. The proposed GAs are tested on two sets of benchmark problems in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed GAs are able to produce promising results for the UCTP.This work was supported by the Engineering and Physical Sciences Research Council of U.K. under Grant EP/E060722/1

Recent Advances in Energy Time Series Forecasting

This editorial summarizes the performance of the special issue entitled Energy Time Series Forecasting, which was published in MDPI’s Energies journal. The special issue took place in 2016 and accepted a total of 21 papers from twelve different countries. Electrical, solar, or wind energy forecasting were the most analyzed topics, introducing brand new methods with very sound results.Ministerio de Economía y Competitividad TIN2014-55894-C2-

Complexity Measures and Models in Supply Chain Networks

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Editorial Special Issue: Logistics, networks and sustainability in freight transport

The papers in this Special Issue provide several analyses and descriptions of the relevance of logistics, networks and sustainability in the recent evolution of freight transport flows at the local, national, and global levels. This phenomenon has been determined by several causes. First, the augmented GDP per capita in developed economies has stimulated the demand for an ever-increasing amount and variety of goods. Moreover, a significant share of the productive processes has been outsourced from the developed countries towards the developing ones. The economic reasons that have fostered this delocalization can be traced back to lower labour and investment costs in the latter countries, together with the decrease in unit transport costs. There has consequently been an increase in the complexity of existing freight transport networks and the emergence of new ones (Van Geenhuizen et al., 2007). This evolution has partly been related to the behaviour of logistics firms that, faced with an ever-rising level of competition, have been looking for alternative transport networks involving the implementation of peculiar transport means or of efficient intermodal systems (Beuthe et al., 2004). Moreover, as was mentioned above, new transport networks have emerged on the basis of evolving consumers’ demands and productive processes. This has, on the one hand, implied the urge for logistics firms to reconsider their strategic decisions (Brewer et al., 2001; De Kok and Graves, 2003) and, on the other hand, the opportunity for local and national administrations to take advantage of the international and/or intercontinental hubs of freight transport networks located within their territories (Banister, 2002)