scholarly journals Forecasting the OECD Fixed Broadband Penetration with Genetic Programming Method, Diffusion Models and Macro-Economic Indicators

2016 ◽  
Vol 21 (1) ◽  
pp. 47-55
Author(s):  
Konstantinos Salpasaranis ◽  
Vasilios Stylianakis
Keyword(s):  
Genetic Programming ◽  
High Performance ◽  
Oecd Countries ◽  
Diffusion Models ◽  
Causal Models ◽  
Programming Method ◽  
Economic Indicators ◽  
Combination Method ◽  
Technological Forecasting ◽  
Statistical Indicators

Abstract This paper presents the implementation of a modified Genetic Programming (GP) method in forecasting fixed broadband telecommunications penetration percentage in Organisation for Economic Co-operation and Development (OECD) countries. The specific GP method combines the use of known diffusion models for technological forecasting purposes, such as Logistic, Gompertz and Bass and the GP. The combination method produces both time dependant and causal models with high performance statistical indicators. Also, multiple approaches to forecasting can be implemented, mainly with no big datasets.

scholarly journals Combining Diffusion Models and Macroeconomic Indicators with a Modified Genetic Programming Method: Implementation in Forecasting the Number of Mobile Telecommunications Subscribers in OECD Countries

2014 ◽  
Vol 2014 ◽  
pp. 1-20 ◽  
Author(s):  
Konstantinos Salpasaranis ◽  
Vasilios Stylianakis ◽  
Stavros Kotsopoulos
Keyword(s):  
Genetic Programming ◽  
Oecd Countries ◽  
Diffusion Models ◽  
Programming Method ◽  
Initial Population ◽  
Mobile Telecommunications ◽  
Technological Forecasting ◽  
Consumer Prices ◽  
Selection Indices ◽  
Macroeconomic Indicators

This paper proposes a modified Genetic Programming method for forecasting the mobile telecommunications subscribers’ population. The method constitutes an expansion of the hybrid Genetic Programming (hGP) method improved by the introduction of diffusion models for technological forecasting purposes in the initial population, such as the Logistic, Gompertz, and Bass, as well as the Bi-Logistic and LogInLog. In addition, the aforementioned functions and models expand the function set of hGP. The application of the method in combination with macroeconomic indicators such as Gross Domestic Product per Capita (GDPpC) and Consumer Prices Index (CPI) leads to the creation of forecasting models and scenarios for medium- and long-term level of predictability. The forecasting module of the program has also been improved with the multi-levelled use of the statistical indices as fitness functions and model selection indices. The implementation of the modified-hGP in the datasets of mobile subscribers in the Organisation for Economic Cooperation and Development (OECD) countries shows very satisfactory forecasting performance.

scholarly journals A Hybrid Genetic Programming Method in Optimization and Forecasting: A Case Study of the Broadband Penetration in OECD Countries

2012 ◽  
Vol 2012 ◽  
pp. 1-32 ◽  
Author(s):  
Konstantinos Salpasaranis ◽  
Vasilios Stylianakis
Keyword(s):  
Genetic Programming ◽  
High Performance ◽  
Diffusion Models ◽  
Programming Method ◽  
Initial Population ◽  
Penetration Data ◽  
Python Programming ◽  
User Friendly ◽  
Statistical Indicators

The introduction of a hybrid genetic programming method (hGP) in fitting and forecasting of the broadband penetration data is proposed. The hGP uses some well-known diffusion models, such as those of Gompertz, Logistic, and Bass, in the initial population of the solutions in order to accelerate the algorithm. The produced solutions models of the hGP are used in fitting and forecasting the adoption of broadband penetration. We investigate the fitting performance of the hGP, and we use the hGP to forecast the broadband penetration in OECD (Organisation for Economic Co-operation and Development) countries. The results of the optimized diffusion models are compared to those of the hGP-generated models. The comparison indicates that the hGP manages to generate solutions with high-performance statistical indicators. The hGP cooperates with the existing diffusion models, thus allowing multiple approaches to forecasting. The modified algorithm is implemented in the Python programming language, which is fast in execution time, compact, and user friendly.

RECOMMENDATIONS FOR THE CHOICE OF MILKING INSTALLATIONS IN LOOSE HOUSING SYSTEMS OF COWS

2020 ◽  
Author(s):  
В.В. ГОРДЕЕВ ◽  
В.Е. ХАЗАНОВ
Keyword(s):  
Dairy Cows ◽  
High Performance ◽  
Large Scale ◽  
Dairy Farms ◽  
Economic Indicators ◽  
Technical Level ◽  
Housing Systems ◽  
Working Shift ◽  
Technical And Economic Indicators

При выборе типа доильной установки и ее размера необходимо учитывать максимальное планируемое поголовье дойных коров и размер технологической группы, кратность и время одного доения, продолжительность рабочей смены дояров. Анализ технико-экономических показателей наиболее распространенных на сегодняшний день типов доильных установок одинакового технического уровня свидетельствует, что наилучшие удельные показатели имеет установка типа «Карусель» (1), а установка типа «Елочка» (2) требует более высоких затрат труда и средств. Установка «Параллель» (3) занимает промежуточное положение. Из анализа пропускной способности и количества необходимых операторов: установка 2 рекомендована для ферм с поголовьем дойного стада до 600 голов, 3 — не более 1200 дойных коров, 1 — более 1200 дойных коров. «Карусель» — наиболее рациональный, высокопроизводительный, легко автоматизируемый и, следовательно, перспективный способ доения в залах, особенно для крупных молочных ферм. The choice of the proper type and size of milking installations needs to take into account the maximum planned number of dairy cows, the size of a technological group, the number of milkings per day, and the duration of one milking and the operator's working shift. The analysis of technical and economic indicators of currently most common types of milking machines of the same technical level revealed that the Carousel installation had the best specific indicators while the Herringbone installation featured higher labour inputs and cash costs. The Parallel installation was found somewhere in between. In terms of the throughput and the required number of operators Herringbone is recommended for farms with up to 600 dairy cows, Parallel — below 1200 dairy cows, Carousel — above 1200 dairy cows. Carousel was found the most practical, high-performance, easily automated and, therefore, promising milking system for milking parlours, especially on the large-scale dairy farms.

scholarly journals Improving probabilistic flood forecasting through a data assimilation scheme based on genetic programming

2012 ◽  
Vol 12 (12) ◽  
pp. 3719-3732 ◽  
Author(s):  
L. Mediero ◽  
L. Garrote ◽  
A. Chavez-Jimenez
Keyword(s):  
Data Assimilation ◽  
Genetic Programming ◽  
Real Time ◽  
High Performance ◽  
Forecast Model ◽  
Flood Forecasting ◽  
Distribution Functions ◽  
Model Parameters ◽  
Hydrological Process ◽  
Model Based

Abstract. Opportunities offered by high performance computing provide a significant degree of promise in the enhancement of the performance of real-time flood forecasting systems. In this paper, a real-time framework for probabilistic flood forecasting through data assimilation is presented. The distributed rainfall-runoff real-time interactive basin simulator (RIBS) model is selected to simulate the hydrological process in the basin. Although the RIBS model is deterministic, it is run in a probabilistic way through the results of calibration developed in a previous work performed by the authors that identifies the probability distribution functions that best characterise the most relevant model parameters. Adaptive techniques improve the result of flood forecasts because the model can be adapted to observations in real time as new information is available. The new adaptive forecast model based on genetic programming as a data assimilation technique is compared with the previously developed flood forecast model based on the calibration results. Both models are probabilistic as they generate an ensemble of hydrographs, taking the different uncertainties inherent in any forecast process into account. The Manzanares River basin was selected as a case study, with the process being computationally intensive as it requires simulation of many replicas of the ensemble in real time.

Two-Run Genetic Programming for Predicting Slump Flow of Concrete

2014 ◽  
Vol 590 ◽  
pp. 321-325
Author(s):  
Li Chen ◽  
Chang Huan Kou ◽  
Kuan Ting Chen ◽  
Shih Wei Ma
Keyword(s):  
Nonlinear Systems ◽  
Genetic Programming ◽  
High Performance ◽  
Mean Squared Error ◽  
High Performance Concrete ◽  
Local Optimum ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Slump Flow ◽  
Low Dimensional

A two-run genetic programming (GP) is proposed to estimate the slump flow of high-performance concrete (HPC) using several significant concrete ingredients in this study. GP optimizes functions and their associated coefficients simultaneously and is suitable to automatically discover relationships between nonlinear systems. Basic-GP usually suffers from premature convergence, which cannot acquire satisfying solutions and show satisfied performance only on low dimensional problems. Therefore it was improved by an automatically incremental procedure to improve the search ability and avoid local optimum. The results demonstrated that two-run GP generates an accurate formula through and has 7.5 % improvement on root mean squared error (RMSE) for predicting the slump flow of HPC than Basic-GP.

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