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کارایی بیت کوین: یک رویکرد برنامه نویسی ژنتیکی قوی برای بازارهای الکترونیکی هوشمند بیت کوین
سال انتشار: 2021 - تعداد صفحات فایل pdf انگلیسی: 14 - تعداد صفحات فایل doc فارسی: 47 از زمانی که بیت کوین برای اولین بار توسط ساتوشی ناکاموتو در سال 2008 پیشنهاد شد، ارزهای دیجیتال توجه زیادی را به خود جلب کردند و پتانسیل ایفای نقش مهمی در تجارت الکترونیک را برجسته کردند. با این حال، اطلاعات نسبتا کمی در مورد ارزهای دیجیتال، رفتار قیمتی آنها، سرعت ترکیب اطلاعات جدید و کارایی بازار مربوطه آنها وجود دارد. برای گسترش ادبیات فعلی در این زمینه، ما چهار بازار هوشمند بیت کوین الکترونیکی را با انواع مختلف معامله گران با استفاده از یک فرم تطبیقی خاص از الگوریتم یادگیری مبتنی بر برنامه نویسی ژنتیکی تایپ شده قوی (STGP) توسعه می دهیم. ما تکنیک STGP را برای داده های تاریخی بیت کوین در فرکانس های یک دقیقه و پنج دقیقه اعمال می کنیم تا شکل گیری پویایی بازار بیت کوین و کارایی بازار را بررسی کنیم. از طریق انبوهی از روشهای تست قوی، متوجه میشویم که هر دو بازار بیتکوین پر از معاملهگران با فرکانس بالا (HFT) در فرکانس یک دقیقه کارآمد هستند اما در فرکانس پنج دقیقه ناکارآمد هستند. این یافته از این استدلال حمایت می کند که در فرکانس یک دقیقه سرمایه گذاران می توانند اطلاعات جدید را به شیوه ای سریع و منطقی ترکیب کنند و از نویز مرتبط با فرکانس پنج دقیقه رنج نبرند. ما همچنین با نشان دادن اینکه معاملهگران با هوش صفر نمیتوانند به کارایی بازار برسند، به ادبیات تجارت الکترونیک کمک میکنیم، بنابراین شواهدی علیه فرضیه هی ارائه میکنیم. یکی از پیامدهای عملی این مطالعه این است که ما نشان میدهیم که متخصصان تجارت الکترونیک میتوانند از ابزارهای هوش مصنوعی مانند STGP برای انجام پروفایل بازار مبتنی بر رفتار استفاده کنند.
کلمات کلیدی: هوش مصنوعی | بازارهای الکترونیک هوشمند | تجارت بیت کوین | ارزهای دیجیتال | محاسبات تکاملی | کارایی بازار |
مقاله ترجمه شده |
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Flexibility management model of home appliances to support DSO requests in smart grids
مدل مدیریت انعطاف پذیری لوازم خانگی برای پشتیبانی از درخواست DSO در شبکه های هوشمند-2020 Several initiates have been taken promoting clean energy and the use of local flexibility towards a more sustainable
and green economy. From a residential point of view, flexibility can be provided to operators using
home-appliances with the ability to modify their consumption profiles. These actions are part of demand response
programs and can be utilized to avoid problems, such as balancing/congestion, in distribution networks.
In this paper, we propose a model for aggregators flexibility provision in distribution networks. The model takes
advantage of load flexibility resources allowing the re-schedule of shifting/real-time home-appliances to provision
a request from a distribution system operator (DSO) or a balance responsible party (BRP). Due to the
complex nature of the problem, evolutionary computation is evoked and different algorithms are implemented
for solving the formulation efficiently. A case study considering 20 residential houses equipped each with seven
types of home-appliances is used to test and compare the performance of evolutionary algorithms solving the
proposed model. Results show that the aggregator can fulfill a flexibility request from the DSO/BRP by rescheduling
the home-appliances loads for the next 24-h horizon while minimizing the costs associated with the
remuneration given to end-users Keywords: Demand response | Flexibility | Home appliances | Local energy management | Smart grids |
مقاله انگلیسی |
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A genetic Artificial Bee Colony algorithm for signal reconstruction based big data optimization
یک الگوریتم ژنتیکی زنبورعسل مصنوعی برای بهینه سازی داده های بزرگ مبتنی بر بازسازی سیگنال-2020 In recent years, the researchers have witnessed the changes or transformations driven by the existence
of the big data on the definitions, complexities and future directions of the real world optimization
problems. Analyzing the capabilities of the previously introduced techniques, determining possible
drawbacks of them and developing new methods by taking into consideration of the unique properties
related with the big data are nowadays in urgent demands. Artificial Bee Colony (ABC) algorithm
inspired by the clever foraging behaviors of the real honey bees is one of the most successful
swarm intelligence based optimization algorithms. In this study, a novel ABC algorithm based big data
optimization technique was proposed. For exploring the solving abilities of the proposed technique,
a set of experimental studies has been carried out by using different signal decomposition based
big data optimization problems presented at the Congress on Evolutionary Computation (CEC) 2015
Big Data Optimization Competition. The results obtained from the experimental studies first were
compared with the well-known variants of the standard ABC algorithm named gbest-guided ABC
(GABC), ABC/best/1, ABC/best/2, crossover ABC (CABC), converge-onlookers ABC (COABC) and quick ABC
(qABC). The results of the proposed ABC algorithm were also compared with the Differential Evolution
(DE) algorithm, Genetic algorithm (GA), Firefly algorithm (FA), Fireworks algorithm (FW), Phase Base
Optimization (PBO) algorithm, Particle Swarm Optimization (PSO) algorithm and Dragonfly algorithm
(DA) based big data optimization techniques. From the experimental studies, it was understood that the
newly introduced ABC algorithm based technique is capable of producing better or at least promising
results compared to the mentioned big data optimization techniques for all of the benchmark instances. Keywords: Big data optimization | Signal decomposition | Artificial Bee Colony |
مقاله انگلیسی |
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FBI inspired meta-optimization
اف بی آی الهام گرفته از متا بهینه سازی-2020 This study developed a novel optimization algorithm, called Forensic-Based Investigation (FBI), inspired
by the suspect investigation–location–pursuit process that is used by police officers. Although
numerous unwieldy optimization algorithms hamper their usability by requiring predefined operating
parameters, FBI is a user-friendly algorithm that does not require predefined operating parameters.
The performance of parameter-free FBI was validated using four experiments: (1) The robustness and
efficiency of FBI were compared with those of 12 representations of the top leading metaphors by
using 50 renowned multidimensional benchmark problems. The result indicated that FBI remarkably
outperformed all other algorithms. (2) FBI was applied to solve a resource-constrained scheduling
problem associated with a highway construction project. The experiment demonstrated that FBI
yielded the shortest schedule with a success rate of 100%, indicating its stability and robustness.
(3) FBI was utilized to solve 30 benchmark functions that were most recently presented at the
IEEE Congress on Evolutionary Computation (CEC) competition on bound-constrained problems. Its
performance was compared with those of the three winners in CEC to validate its effectiveness.
(4) FBI solved high-dimensional problems, by increasing the number of dimensions of benchmark
functions to 1000. FBI is efficient because it requires a relatively short computational time for solving
problems, it reaches the optimal solution more rapidly than other algorithms, and it efficaciously solves
high-dimensional problems. Given that the experiments demonstrated FBI’s robustness, efficiency,
stability, and user-friendliness, FBI is promising for solving various complex problems. Finally, this
study provided the scientific community with a metaheuristic optimization platform for graphically
and logically manipulating optimization algorithms. Keywords: Forensic-based investigation algorithm | Metaheuristic optimization | Swarm intelligence and evolutionary | computation | Benchmark functions | Construction engineering and project | management |
مقاله انگلیسی |
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An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction
یک مدل ماشین پیشرفته افراطی برای پیش بینی جریان رودخانه: پیشرفته ترین برنامه ها ، کاربردهای عملی در منطقه مهندسی منابع آب و جهت گیری تحقیقات آینده-2019 Despite the massive diversity in the modeling requirements for practical hydrological applications, there remains
a need to develop more reliable and intelligent expert systems used for real-time prediction purposes. The
challenge in meeting the standards of an expert system is primarily due to the influence and behavior of hydrological
processes that is driven by natural fluctuations over the physical scale, and the resulting variance in
the underlying model input datasets. River flow forecasting is an imperative task for water resources operation
and management, water demand assessments, irrigation and agriculture, early flood warning and hydropower
generations. This paper aims to investigate the viability of the enhanced version of extreme learning machine
(EELM) model in river flow forecasting applied in a tropical environment. Herein, we apply the complete orthogonal
decomposition (COD) learning tool to tune the output-hidden layer of the ELM model’s internal neuronal
system, instead of the conventional multi-resolution tool (e.g., singular value decomposition). ToA-ELM, AdaBoost.RT-extreme learning machine; AI, artificial intelligence; ANFIS, adaptive neuro-fuzzy inference system; ANN, artificial neural
network; ARIMA, autoregressive integrated moving average; AtmP, atmospheric pressure; B-ANN, bootstrap-artificial neural network; BCSO, binary-coded swarm
optimization; B-ELM, bootstrap-extreme learning machine; C-ELM, complex-extreme learning machine; Cl−1, chloride; COD, complete orthogonal decomposition
(COD); CRO-ELM, coral reefs optimization-extreme learning machine; DE-ELM, deferential evolution-extreme learning machine; DID, department of Irrigation and
Drainage; DO, dissolved oxygen concentration; EC-SVR, evolutionary computation-based support vector machine; EDI, effective drought index; ELM, extreme
learning machine; EELM, enhanced extreme learning machine; EEMD, ensemble empirical mode decomposition; EL-ANFIS, extreme learning adaptive neuro-fuzzy
inference system; EMD, empirical mode decomposition; Ens, Nash-Sutcliffe coefficient; Ensemble-ELM, ensemble-extreme learning machine; EPR, evolutionary
polynomial regression; ESNs, echo state networks; ETo, evapotranspiration; Fe, iron; Fr, Froude number; FS, factor of safety; GA-ELM, genetic algorithm-extreme
learning machine; GCM, general circulation model; G-ELM, geomorphology extreme learning machine; GP, genetic programming; GRNN, generalized regression
neural network; HCO3
-1, bicarbonate; HDSR, diffuse solar radiation; HRT, hydraulic retention time; I-ELM, integrated extreme learning machine; KELM, Kernelextreme
learning machine; LST, land surface temperature; LASSO, least absolute shrinkage and selection operator; LSTM, long short-term memory network; LSSVM,
least square support vector machine; MAE, mean absolute error; MARS, multivariate adaptive regression spline; MBFIPS, Multi-objective binary-coded fully informed
particle swarm optimization; MC-OS-ELM, meta cognitive-online sequential-extreme learning machine; MLPNN, multi-linear perceptron neural network; MLR,
multiple linear regression; MME, multi-model ensemble; NEMR, northeast monsoon rainfall; NO2
-1, nitrite; NO3
-1, nitrate; NO2, nitrogen dioxide; NT, total nitrogen;
O3, ozone; OP-ELM, optimally pruned-extreme learning machine; OSELM, online sequential extreme learning machine; PCA, principal component analysis; pH,
power of hydrogen; PM10, air pollution “suspended particulate matters”; PO4
-3, phosphorus; R-ELM, radial basis-extreme learning machine; r, determination coefficient;
RE, relative error; RF, rainfall; RH, relative humidity; RHmax, maximum relative humidity; RHmean, mean relative humidity; RHmin, minimum relative
humidity; RMSE, root mean square error; RVM, relevance vector machine; SaE-ELM, self-adaptive evolutionary-extreme learning machine; SC, specific conductance;
S-ELM, sigmoid-extreme learning machine; SHr, sunshine hour; SR, solar radiation; SO4
-2, sulfate; SiO2, Silicon; SO2, |
مقاله انگلیسی |
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Implementation of nature-inspired optimization algorithms in some data mining tasks
اجرای الگوریتم های بهینه سازی با الهام از طبیعت در برخی از کارهای داده کاوی-2019 Data mining optimization received much attention in the last decades due to introducing new optimization
techniques, which were applied successfully to solve such stochastic mining problems. This paper
addresses implementation of evolutionary optimization algorithms (EOAs) for mining two famous data
sets in machine learning by implementing four different optimization techniques. The selected data sets
used for evaluating the proposed optimization algorithms are Iris dataset and Breast Cancer dataset. In
the classification problem of this paper, the neural network (NN) is used with four optimization techniques,
which are whale optimization algorithm (WOA), dragonfly algorithm (DA), multiverse optimization
(MVA), and grey wolf optimization (GWO). Different control parameters were considered for
accurate judgments of the suggested optimization techniques. The comparitive study proves that, the
GWO, and MVO provide accurate results over both WO, and DA in terms of convergence, runtime, classification
rate, and MSE. Keywords: Data mining | Optimization | Evolutionary computation | Multi-layer perceptron | Metaheuristics |
مقاله انگلیسی |
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An evolutionary framework for machine learning applied to medical data
یک چارچوب تکاملی برای یادگیری ماشین که برای داده های پزشکی کاربرد دارد-2019 Supervised learning problems can be faced by using a wide variety of approaches supported in
machine learning. In recent years there has been an increasing interest in using the evolutionary
computation paradigm as a search method for classifiers, helping the applied machine learning
technique. In this context, the knowledge representation in the form of logical rules has been one
of the most accepted machine learning approaches, because of its level of expressiveness. This paper
proposes an evolutionary framework for rule-based classifier induction. Our proposal introduces
genetic programming to build a search method for classification-rules (IF/THEN). From this approach,
we deal with problems such as, maximum rule length and rule intersection. The experiments have
been carried out on our domain of interest, medical data. The achieved results define a methodology
to follow in the learning method evaluation for knowledge discovery from medical data. Moreover, the
results compared to other methods have shown that our proposal can be very useful in data analysis
and classification coming from the medical domain. Keywords: Machine learning | Logical rule induction | Data mining | Supervised learning | Evolutionary computation | Genetic programming | Ensemble classifier | Medical data |
مقاله انگلیسی |
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A distributed evolutionary multivariate discretizer for Big Data processing on Apache Spark
یک توزیع تکاملی چند متغیره برای پردازش داده های بزرگ در Apache Spark-2018 Nowadays the phenomenon of Big Data is overwhelming our capacity to extract relevant knowledge through
classical machine learning techniques. Discretization (as part of data reduction) is presented as a real solution to
reduce this complexity. However, standard discretizers are not designed to perform well with such amounts of
data. This paper proposes a distributed discretization algorithm for Big Data analytics based on evolutionary
optimization. After comparing with a distributed discretizer based on the Minimum Description Length
Principle, we have found that our solution yields more accurate and simpler solutions in reasonable time.
Keywords: Discretizacion , Evolutionary computation , Big Data , Data Mining , Apache Spark |
مقاله انگلیسی |
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Active control for traffic lights in regions and corridors: an approach based on evolutionary computation
کنترل فعال برای چراغ های راهنمایی در مناطق و راهرو: یک رویکرد مبتنی بر محاسبات تکاملی-2017 The growth of vehicles’ fleet circulating on urban streets constitutes a very strong tendency in recent years. The main
consequence of this phenomenon refers to the increase of urban congestions, of average delays caused by vehicles waiting on
traffic lights and of number of stops. Finding strategies to achieve efficient active traffic control in urban centers is a challenge
for engineers and analysts. Recently, important research on dynamic networks and Intelligent Transportation Systems using
computational intelligence modeling techniques has been done. This paper proposes a new scheme of active control, using
optimization algorithms, to dynamically find traffic signal control plans that optimize traffic conditions in delimited networks and
corridors. The proposed system includes a time delay predictive model, used in conjunction with evolutionary approaches like
genetic algorithms and differential evolution techniques. Conceptual and applied computational representations necessary for the
construction of models are presented. Data collected from a big city in Brazil were fed into the commercial microscopic
simulator AIMSUN and were used for the practical experiments. Two main experiments were undertaken and statistically
compared in order to decide which method is more efficient in optimizing the active traffic signal timing control for the region
under study.
Keywords: intelligent transportation systems | traffic lights programming | evolutionary algorithms | optimization | active traffic control |
مقاله انگلیسی |
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Flocking based evolutionary computation strategy for measuring centrality of online social networks
استراتژی محاسبات تکاملی مبتنی بر Flocking برای اندازه گیری مرکزیت شبکه های اجتماعی آنلاین-2017 Centrality in social network is one of the major research topics in social network analysis. Even though
there are more than half a dozen methods to find centrality of a node, each of these methods has some
drawbacks in one aspect or the other. This paper analyses different centrality calculation methods and
proposes a new swarm based method named Flocking Based Centrality for Social network (FBCS). This
new computation technique makes use of parameters that are more realistic and practical in online social
networks. The interactions between nodes play a significant role in determining the centrality of node. The
new method has been calculated both empirically as well as experimentally. The new method is tested,
verified and validated for different sets of random networks and benchmark datasets. The method has
been correlated with other state of the art centrality measures. The new centrality measure is found to
be realistic and suits well with online social networks. The proposed method can be used in applications
such as finding the most prestigious node and for discovering the node which can influence maximum
number of users in an online social network. FBCS centrality has higher Kendall’s tau correlation when
compared with other state of the art centrality methods. The robustness of the FBCS centrality is found
to be better than other centrality measures.
Key Terms: Centrality in social network | Degree of nodes | Online social network analysis | Boid’s algorithm | Flocking of birds |
مقاله انگلیسی |