Scopus İndeksli Yayınlar Koleksiyonu

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Access Right: Closed AccessSubject: search.filters.subject.Genetic Algorithms

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Now showing 1 - 6 of 6
  • Article
    Citation - WoS: 5
    Citation - Scopus: 4
    Uv-Visible Spectrophotometric Quantitative Analysis of Ternary Mixture Using Multivariate Calibration Methods Optimized by a Genetic Algorithm
    (Chiminform Data S A, 2010) Ozdemir, Durmus; Baleanu, Dumitru; Dinc, Erdal; Baleanu, Dumitru; Matematik
    Simultaneous determination of ternary mixtures of caffeine, paracetamol and metamizol in commercial tablet formulations using UV-visible spectro photometry combined with classical least squares (CLS) and genetic algorithm (GA) based multivariate calibration methods were demonstrated. The three genetic multivariate calibration methods are named as Genetic Classical Least Squares (GCLS), Genetic Inverse Least Squares (GILS) and Genetic Regression (GR). The GR method is based on a genetic algorithm based wavelength selection followed by a simple linear regression step whereas the GCLS and GILS are multivariate calibration methods modified by a wavelength selection principle using a genetic algorithm. The sample data set contains the UV-visible spectra of 47 synthetic mixtures (4 to 48 mu g/mL) and 16 tablets containing these components from two different producers. The spectra cover the range from 200 to 330 nm in 0.1 nm intervals. Several calibration models were built with the four methods for the three components. Overall, the standard error of calibration (SEC) and the standard error of prediction (SEP) for the synthetic data were in the range of 0.04 and 2.34 mu g/mL for all the four methods. Predictive ability of the calibration models generated with synthetic samples was tested with actual tablet samples and results obtained from four methods were compared. The SEP values for the tablets were in the range of 0.31 and 15.44 mg/tablets.
  • Article
    Citation - WoS: 5
    Citation - Scopus: 9
    Strategy Creation, Decomposition and Distribution in Particle Navigation
    (Elsevier Science inc, 2007) Leblebicioglu, Kemal; Beldek, Ulas
    Strategy planning is crucial to control a group to achieve a number of tasks in a closed area full of obstacles. In this study, genetic programming has been used to evolve rule-based hierarchical structures to move the particles in a grid region to accomplish navigation tasks. Communications operations such as receiving and sending commands between particles are also provided to develop improved strategies. In order to produce more capable strategies, a task decomposition procedure is proposed. In addition, a conflict module is constructed to handle the challenging situations and conflicts such as blockage of a particle's pathway to destination by other particles. (C) 2006 Elsevier Inc. All rights reserved.
  • Article
    Citation - Scopus: 7
    Dynamics of Three-Point Boundary Value Problems With Gudermannian Neural Networks
    (Springer Science and Business Media Deutschland GmbH, 2023) Raja, M.A.Z.; Sadat, R.; Baleanu, D.; Sabir, Z.; Ali, M.R.
    The present study articulates a novel heuristic computing design with artificial intelligence algorithm by manipulating the models with Feed forward (FF) Gudermannian neural networks (GNN) accomplished with global search capability of Genetic algorithms (GA) combined with rapid local convergence of Active-set method (ASM), i.e., FF-GNN-GAASM for solving the second kind of Three-point singular boundary value problems (TPS-BVPs). The proposed FF-GNN-GAASM intelligent computing solver integrated into the hidden layer structure of FF-GNN systems of differential operatives of the second kind of STP-BVPs, which are linked to form the error based Merit function (MF). The MF is optimized with the hybrid-combined heuristics of GAASM. The stimulation for presenting this research work comes from the objective to introduce a reliable framework that associates the operational features of NNs to challenge with such inspiring models. Three different measures of the second kind of TPS-BVPs is applied to assess the robustness, correctness and usefulness of the designed FF-GNN-GAASM. Statistical evaluations through the performance of FF-GNN-GAASM is validated via consistent stability, accuracy and convergence. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
  • Article
    Citation - WoS: 12
    Citation - Scopus: 15
    Numerical Solutions of a Novel Designed Prevention Class in the Hiv Nonlinear Model
    (Tech Science Press, 2021) Umar, Muhammad; Raja, Muhammad Asif Zahoor; Baleanu, Dumitru; Sabir, Zulqurnain
    The presented research aims to design a new prevention class (P) in the HIV nonlinear system, i.e., the HIPV model. Then numerical treatment of the newly formulated HIPV model is portrayed handled by using the strength of stochastic procedure based numerical computing schemes exploiting the artificial neural networks (ANNs) modeling legacy together with the optimization competence of the hybrid of global and local search schemes via genetic algorithms (GAs) and active-set approach (ASA), i.e., GA-ASA. The optimization performances through GA-ASA are accessed by presenting an error-based fitness function designed for all the classes of the HIPV model and its corresponding initial conditions represented with nonlinear systems of ODEs. To check the exactness of the proposed stochastic scheme, the comparison of the obtained results and Adams numerical results is performed. For the convergence measures, the learning curves are presented based on the different contact rate values. Moreover, the statistical performances through different operators indicate the stability and reliability of the proposed stochastic scheme to solve the novel designed HIPV model.
  • Article
    Citation - WoS: 62
    Citation - Scopus: 53
    Design of Stochastic Numerical Solver for the Solution of Singular Three-Point Second-Order Boundary Value Problems
    (Springer London Ltd, 2021) Baleanu, Dumitru; Shoaib, Muhammad; Raja, Muhammad Asif Zahoor; Sabir, Zulqurnain
    In this paper, a novel meta-heuristic computing solver is presented for solving the singular three-point second-order boundary value problems using artificial neural networks (ANNs) optimized by the combined strength of global and local search ability of genetic algorithms (GAs) and interior point algorithm (IPA), i.e., ANN-GA-IPA. The inspiration for presenting this numerical work comes from the intention of introducing a consistent framework that combines the effective features of neural networks optimized with the contexts of soft computing to handle with such challenging systems. Three numerical variants of singular second-order system have been taken to examine the proficiency, robustness, and stability of the designed approach. The comparison of the proposed results of ANN-GA-IPA from available exact solutions shows the good agreement with 5 to 7 decimal places of the accuracy which established worth of the methodology through performance analyses on a single and multiple executions.
  • Article
    Citation - WoS: 28
    Citation - Scopus: 31
    Customer Order Scheduling Problem: a Comparative Metaheuristics Study
    (Springer London Ltd, 2008) Hazir, Oncue; Gunalay, Yavuz; Erel, Erdal
    The customer order scheduling problem (COSP) is defined as to determine the sequence of tasks to satisfy the demand of customers who order several types of products produced on a single machine. A setup is required whenever a product type is launched. The objective of the scheduling problem is to minimize the average customer order flow time. Since the customer order scheduling problem is known to be strongly NP-hard, we solve it using four major metaheuristics and compare the performance of these heuristics, namely, simulated annealing, genetic algorithms, tabu search, and ant colony optimization. These are selected to represent various characteristics of metaheuristics: nature-inspired vs. artificially created, population-based vs. local search, etc. A set of problems is generated to compare the solution quality and computational efforts of these heuristics. Results of the experimentation show that tabu search and ant colony perform better for large problems whereas simulated annealing performs best in small-size problems. Some conclusions are also drawn on the interactions between various problem parameters and the performance of the heuristics.