Multi-step time series prediction intervals using neuroevolution

Multi-step time series forecasting (TSF) is a crucial element to support tactical decisions (e.g., designing production or marketing plans several months in advance). While most TSF research addresses only single-point prediction, prediction intervals (PIs) are useful to reduce uncertainty related to important decision making variables. In this paper, we explore a large set of neural network methods for multi-step TSF and that directly optimize PIs. This includes multi-step adaptations of recently proposed PI methods, such as lower--upper bound estimation (LUBET), its ensemble extension (LUBEXT), a multi-objective evolutionary algorithm LUBE (MLUBET) and a two-phase learning multi-objective evolutionary algorithm (M2LUBET). We also explore two new ensemble variants for the evolutionary approaches based on two PI coverage--width split methods (radial slices and clustering), leading to the MLUBEXT, M2LUBEXT, MLUBEXT2 and M2LUBEXT2 methods. A robust comparison was held by considering the rolling window procedure, nine time series from several real-world domains and with different characteristics, two PI quality measures (coverage error and width) and the Wilcoxon statistic. Overall, the best results were achieved by the M2LUBET neuroevolution method, which requires a reasonable computational effort for time series with a few hundreds of observations.This article is a result of the project NORTE-01- 0247-FEDER-017497, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). We would also like to thank the anonymous reviewers for their helpful suggestionsinfo:eu-repo/semantics/publishedVersio

Identifying efficient solutions via simulation: myopic multi-objective budget allocation for the bi-objective case

Simulation optimisation offers great opportunities in the design and optimisation of complex systems. In the presence of multiple objectives, there is usually no single solution that performs best on all objectives. Instead, there are several Pareto-optimal (efficient) solutions with different trade-offs which cannot be improved in any objective without sacrificing performance in another objective. For the case where alternatives are evaluated on multiple stochastic criteria, and the performance of an alternative can only be estimated via simulation, we consider the problem of efficiently identifying the Pareto-optimal designs out of a (small) given set of alternatives. We present a simple myopic budget allocation algorithm for multi-objective problems and propose several variants for different settings. In particular, this myopic method only allocates one simulation sample to one alternative in each iteration. This paper shows how the algorithm works in bi-objective problems under different settings. Empirical tests show that our algorithm can significantly reduce the necessary simulation budget

Not all parents are equal for MO-CMA-ES

International audienceThe Steady State variants of the Multi-Objective Covariance Matrix Adaptation Evolution Strategy (SS-MO-CMA-ES) generate one offspring from a uniformly selected parent. Some other parental selection operators for SS-MO-CMA-ES are investigated in this paper. These operators involve the definition of multi-objective rewards, estimating the expectation of the offspring survival and its Hypervolume contribution. Two selection modes, respectively using tournament, and inspired from the Multi-Armed Bandit framework, are used on top of these rewards. Extensive experimental validation comparatively demonstrates the merits of these new selection operators on unimodal MO problems

Effect of roflumilast on inflammatory cells in the lungs of cigarette smoke-exposed mice

<p>Abstract</p> <p>Background</p> <p>We reported that roflumilast, a phosphodiesterase 4 inhibitor, given orally at 5 mg/kg to mice prevented the development of emphysema in a chronic model of cigarette smoke exposure, while at 1 mg/kg was ineffective. Here we investigated the effects of roflumilast on the volume density (V_V) of the inflammatory cells present in the lungs after chronic cigarette smoke exposure.</p> <p>Methods</p> <p>Slides were obtained from blocks of the previous study and V_Vwas assessed immunohistochemically and by point counting using a grid with 48 points, a 20× objective and a computer screen for a final magnification of 580×. Neutrophils were marked with myeloperoxidase antibody, macrophages with Mac-3, dendritic cells with fascin, B-lymphocytes with B220, CD4+ T-cells with CD4+ antibody, and CD8+T-cells with CD8-α. The significance of the differences was calculated using one-way analysis of variance.</p> <p>Results</p> <p>Chronic smoke exposure increased neutrophil V_Vby 97%, macrophage by 107%, dendritic cell by 217%, B-lymphocyte by 436%, CD4+ by 524%, and CD8+ by 417%. The higher dose of roflumilast prevented the increase in neutrophil V_Vby 78%, macrophage by 82%, dendritic cell by 48%, B-lymphocyte by 100%, CD4+ by 98% and CD8+ V_Vby 88%. The lower dose of roflumilast did not prevent the increase in neutrophil, macrophage and B-cell V_Vbut prevented dendritic cells by 42%, CD4+ by 55%, and CD8+ by 91%.</p> <p>Conclusion</p> <p>These results indicate (<it>i</it>) chronic exposure to cigarette smoke in mice results in a significant recruitment into the lung of inflammatory cells of both the innate and adaptive immune system; (<it>ii</it>) roflumilast at the higher dose exerts a protective effect against the recruitment of all these cells and at the lower dose against the recruitment of dendritic cells and T-lymphocytes; (<it>iii</it>) these findings underline the role of innate immunity in the development of pulmonary emphysema and (<it>iiii</it>) support previous results indicating that the inflammatory cells of the adaptive immune system do not play a central role in the development of cigarette smoke induced emphysema in mice.</p

A simulated annealing based genetic local search algorithm for multi-objective multicast routing problems

This paper presents a new hybrid evolutionary algorithm to solve multi-objective multicast routing problems in telecommunication networks. The algorithm combines simulated annealing based strategies and a genetic local search, aiming at a more flexible and effective exploration and exploitation in the search space of the complex problem to find more non-dominated solutions in the Pareto Front. Due to the complex structure of the multicast tree, crossover and mutation operators have been specifically devised concerning the features and constraints in the problem. A new adaptive mutation probability based on simulated annealing is proposed in the hybrid algorithm to adaptively adjust the mutation rate according to the fitness of the new solution against the average quality of the current population during the evolution procedure. Two simulated annealing based search direction tuning strategies are applied to improve the efficiency and effectiveness of the hybrid evolutionary algorithm. Simulations have been carried out on some benchmark multi-objective multicast routing instances and a large amount of random networks with five real world objectives including cost, delay, link utilisations, average delay and delay variation in telecommunication networks. Experimental results demonstrate that both the simulated annealing based strategies and the genetic local search within the proposed multi-objective algorithm, compared with other multi-objective evolutionary algorithms, can efficiently identify high quality non-dominated solution set for multi-objective multicast routing problems and outperform other conventional multi-objective evolutionary algorithms in the literature