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Estimating treatment effects with observational data is very difficult because of contextual confounding, imbalanced case and control group size and etc. In this paper, we present the causal inference methods (propensity score matching estimate and propensity score weighting estimate) that can help to address these problems, aiming at providing unbiased estimates of treatment effects by controlling...
Subgroup discovery is a local pattern mining technique to find interpretable descriptions of sub-populations that stand out on a given target variable. That is, these sub-populations are exceptional with regard to the global distribution. In this paper we argue that in many applications, such as scientific discovery, subgroups are only useful if they are additionally representative of the global distribution...
Code bloat is a phenomenon in Genetic Programming (GP) that increases the size of individuals during the evolutionary process. Over the years, there has been a large number of research that attempted to address this problem. In this paper, we propose a new method to control code bloat and reduce the complexity of the solutions in GP. The proposed method is called Substituting a subtree with an Approximate...
A recent trend in multiobjective evolutionary algorithms is to increase the population size to approximate the Pareto front with high accuracy. On the other hand, the NSGA-II algorithm widely used in multiobjective optimization performs nondominated sorting in solution ranking, which means an increase in computational complexity proportional to the square of the population. This execution time becomes...
Socio-economic characteristics of the systems crisis which was brought about to Russia during the reforms period and was caused by implementing “shock therapy” are examined. Its main negative and positive consequences for the country are noted and the most important reasons for it are systematized. They include both conducting reforms on the basis of “Washington Consensus” provisions and ignoring...
This paper introduces compact music-inspired computing. We propose a music-inspired optimization technique with minimal computational cost. The aim is to reduce the memory storage capacity required by the classical harmony search algorithm (HSA) while improving their performance. Therefore, we propose three compact harmony search algorithms. The main idea is to represent the harmonies stored in the...
Compact evolutionary algorithms (cEAs) are optimization algorithms that require minimal computational cost. They do not require the storage of the population but they represent it by a distribution function. In all known cEAs, normal probability of density function (N-PDF) is used. In this paper, in order to improve the performance of cEAs and to reduce their complicity, we propose a more simple distribution...
This paper presents a comparison of two statistical methodologies that can be used to derive lightning withstand parameters from high-voltage laboratory tests performed on self-restoring insulation. These methodologies are used to determine the 10%, 50%, and 90% probabilities of insulation breakdown associated with a three-phase structure used in compact distribution lines in Brazil. In the performed...
This scientific paper contains the analysis of common MPPT algorithms and usual data processing algorithms for the purpose of the power optimization of mobile solar generators. Furthermore according to the analysis results, an optimized MPPT algorithm, the Global High-Dynamic MPPT algorithm, is designed. The design process approaches, and superordinate algorithm theoretical principles are described.
Nowadays, the attraction of the optimization techniques based on artificial intelligence has increased the due to obtaining the its successful results on the difficult optimization problems in various areas. The artificial bee colony (ABC) algorithm based on swarm intelligence and the differential evolution (DE) algorithm improved by inspiring the natural biological evolution mechanism is the most...
The problem of community detection in complex networks is of high interest in many application domains including sociology, biology, mathematics and economy. Given a set of nodes and links between them, the aim of the problem is to find a grouping of nodes such that a strong community has dense intra-connections and sparse outside community links. In this paper, a coarse-grained evolutionary algorithm...
Technology Enhanced Learning is one of the most dynamic areas of inquiry in education. One form of TELs, that is on-screen learning, has become the topic of interest of many works. It is popular mainly with young people despite all findings, which undoubtedly suggest that it is detrimental to learning. The method hinders learning experience due to the reading spatial instability, difficulties in establishing...
Electricity access is a key enabling factor for human development, yet over 1.2 billion people have no connection. Minigrids, grid extension and standalone systems are the standard approaches to electrification, but each have significant limitations, including cost of energy, speed of deployment and lack of flexible upgrade paths. Interconnection of minigrids and Solar Home Systems (SHS) to allow...
The Adaptive Fourier Decomposition (AFD) is a novel signal decomposition algorithm that can describe an analytical signal through a linear combination of adaptive basis functions. At every decomposition step of the AFD, the basis function is determined by making a search in an over-complete dictionary. The decomposition continues until the difference between the energies of the original and reconstructed...
In this paper capacitor placement problem is solved using a multi-objective approach employing Non-dominated sorting Genetic Algorithm-II (NSGA-II). Loss minimization, voltage deviation minimization and line loading reduction are the objectives which are simultaneously optimized. Uncertainty in load variation is accounted in the capacitor placement problem using Point Estimate Method (PEM). Chance...
This study used MODIS 3 KM Aerosol Optical Depth (AOD) products, ground-level PM2.5 measurements in Beijing and the Public Health knowledge to estimate the number of death attributed to the long-term exposure to a harmful level of PM2.5 concentrations. The study results demonstrated that 2015 population-weighted averaged PM2.5 in Beijing was 70.46 μg/m3, 369.73% exceeding the China's yearly standard...
This paper presents a chaotic particle swarm optimization algorithm with random weights to improve the balancing of a LCD panel assembly line considering the coordination of operation elements. Based on the analysis of the constraints, the performance criteria considered are the line balancing rate and the variation of workload. The results of experiments show that the proposed model produced as good...
Lifestyle disease has been a sprawling issue not only in Malaysia, but worldwide. In the literature, low health literacy and negative health attitude are the two main causal factors for high number of lifestyle disease. Meanwhile, mobile health or better known as mHealth is found to enhance health literacy and encourage positive health attitude. This study aims to find the relationship between health...
This paper presents a proposed cuckoo search algorithm with interactive learning and linear decreasing probability strategy (CSIL) to solve the economic dispatch problem with power balance, prohibited operating zones, valve point effect, and ramp rate. In the new approach, interactive learning strategy helps the nest to exchange good information from each other. Meanwhile, the linear decreasing probability...
The paper suggests an adaptation of a grouping genetic algorithm for solving the capacitated p-median problem. We propose a new encoding of the individual solutions that enables an efficient implementation of the crossover operation. A hybrid metaheuristic that combines the grouping genetic algorithm with the post-processing solver is proposed as well. Numerical experiments performed on benchmark...
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