Yazar "Ari, Davut" seçeneğine göre listele

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    DEHypGpOls: a genetic programming with evolutionary hyperparameter optimization and its application for stock market trend prediction
    (Springer, 2023) Ari, Davut; Alagoz, Baris Baykant
    Stock markets are a popular kind of financial markets because of the possibility of bringing high revenues to their investors. To reduce risk factors for investors, intelligent and automated stock market forecast tools are developed by using computational intelligence techniques. This study presents a hyperparameter optimal genetic programming-based forecast model generation algorithm for a-day-ahead prediction of stock market index trends. To obtain an optimal forecast model from the modeling dataset, a differential evolution (DE) algorithm is employed to optimize hyperparameters of the genetic programming orthogonal least square (GpOls) algorithm. Thus, evolution of GpOls agents within the hyperparameter search space enables adaptation of the GpOls algorithm for the modeling dataset. This evolutionary hyperparameter optimization technique can enhance the data-driven modeling performance of the GpOls algorithm and allow the optimal autotuning of user-defined parameters. In the current study, the proposed DE-based hyper-GpOls (DEHypGpOls) algorithm is used to generate forecaster models for prediction of a-day-ahead trend prediction for the Istanbul Stock Exchange 100 (ISE100) and the Borsa Istanbul 100 (BIST100) indexes. In this experimental study, daily trend data from ISE100 and BIST100 and seven other international stock markets are used to generate a-day-ahead trend forecaster models. Experimental studies on 4 different time slots of stock market index datasets demonstrated that the forecast models of the DEHypGpOls algorithm could provide 57.87% average accuracy in buy-sell recommendations. The market investment simulations with these datasets showed that daily investments to the ISE100 and BIST100 indexes according to buy or sell signals of the forecast model of DEHypGpOls could provide 4.8% more average income compared to the average income of a long-term investment strategy.
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    A differential evolutionary chromosomal gene expression programming technique for electronic nose applications
    (Elsevier, 2023) Ari, Davut; Alagoz, Baris Baykant
    The intelligent system applications require automated data-driven modeling tools. The performance consistency of modeling tools is very essential to reduce the need for human intervention. Classical Gene Expression Programmings (GEPs) employ predefined genetic rules for the node-based evolution of expression trees in the absence of optimal numerical values of constant terminals, and these shortcomings can limit the search efficiency of expression trees. To alleviate negative impacts of these limitations on the data-driven GEP modeling performance, a Differential Evolutionary Chromosomal GEP (DEC-GEP) algorithm is suggested. The DEC-GEP utilizes the Differential Evolution (DE) algorithm for the optimization of a complete genotype of expression trees. For this purpose, a modifier gene container, which stores numerical values of constant terminals, is appended to the frame of GEP chromosome, and this modified chromosome structure enables simultaneous optimization of expression tree genotypes together with numerical values of constant terminals. Besides, the DEC-GEP algorithm can benefit from exploration and exploitation capabilities of the DE algorithm for more efficient evolution of GEP expression trees. To investigate consistency of the DEC-GEP algorithm in a data-driven modeling application, an experimental study was conducted for soft calibration of the low-cost, solid-state sensor array measurements, and results indicated that the DEC-GEP could yield dependable CO concentration estimation models for electronic nose applications.(c) 2023 Elsevier B.V. All rights reserved.
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    An effective integrated genetic programming and neural network model for electronic nose calibration of air pollution monitoring application
    (Springer London Ltd, 2022) Ari, Davut; Alagoz, Baris Baykant
    Air quality control requires real-time monitoring of pollutant concentration distributions in large urban areas. Estimation models are used for the soft-calibration of low-cost multisensor data to improve precision of pollutant concentration measurements. This study introduces an integrated genetic programming dynamic neural network model for more accurate estimation of carbon monoxide and nitrogen dioxide pollutant concentrations from the multisensor measurement data. This model combines a genetic programming-based estimation model with a neural estimator model and improves estimation performances. In this structure, a genetic programming-based polynomial model works as a former estimator and it feeds the neural estimator model via a short-term former estimation memory. Then, the neural model utilizes this former estimation memory in order to enhance pollutant concentration estimations. This integration approach benefits from the correlation enrichment strategy that is performed by the former model. The proposed two-stage training procedure facilitates the training of the integrated models. In experimental study, the standalone genetic programming model, artificial neural network model, and the proposed integrated model are implemented to estimate carbon monoxide and nitrogen dioxide pollutant concentrations from the experimental multisensor air quality data. Results demonstrate that the proposed integrated model can decrease mean relative error about 10% compared to the standalone artificial neural network and about 28% compared to the standalone genetic programming estimation models. Authors suggested that the integrated estimation model can be used for more accurate soft-calibration of multisensor electronic noses in a wide-area air-quality monitoring application.
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    An Evolutionary Field Theorem: Evolutionary Field Optimization in Training of Power-Weighted Multiplicative Neurons for Nitrogen Oxides-Sensitive Electronic Nose Applications
    (Mdpi, 2022) Alagoz, Baris Baykant; Simsek, Ozlem Imik; Ari, Davut; Tepljakov, Aleksei; Petlenkov, Eduard; Alimohammadi, Hossein
    Neuroevolutionary machine learning is an emerging topic in the evolutionary computation field and enables practical modeling solutions for data-driven engineering applications. Contributions of this study to the neuroevolutionary machine learning area are twofold: firstly, this study presents an evolutionary field theorem of search agents and suggests an algorithm for Evolutionary Field Optimization with Geometric Strategies (EFO-GS) on the basis of the evolutionary field theorem. The proposed EFO-GS algorithm benefits from a field-adapted differential crossover mechanism, a field-aware metamutation process to improve the evolutionary search quality. Secondly, the multiplicative neuron model is modified to develop Power-Weighted Multiplicative (PWM) neural models. The modified PWM neuron model involves the power-weighted multiplicative units similar to dendritic branches of biological neurons, and this neuron model can better represent polynomial nonlinearity and they can operate in the real-valued neuron mode, complex-valued neuron mode, and the mixed-mode. In this study, the EFO-GS algorithm is used for the training of the PWM neuron models to perform an efficient neuroevolutionary computation. Authors implement the proposed PWM neural processing with the EFO-GS in an electronic nose application to accurately estimate Nitrogen Oxides (NOx) pollutant concentrations from low-cost multi-sensor array measurements and demonstrate improvements in estimation performance.