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Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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  • Article
    Citation - WoS: 5
    Citation - Scopus: 5
    Taking Advantage of a Diverse Set of Efficient Production Schedules: A Two-Step Approach for Scheduling With Side Concerns
    (Pergamon-Elsevier Science Ltd, 2013-08) Goren, Selcuk; Pierreval, Henri
    In many practical scheduling problems, the concerns of the decision-maker may not be all known in advance and therefore may not be included in the initial problem definition as an objective function and/or as constraints. In such a case, the usual techniques of multi-objective optimization become inapplicable. To cope with this problem and to facilitate handling the concerns of the decision-maker, which can be implicit or qualitative, a dedicated methodological framework is needed. In this paper we propose a new two-step framework. First, we aim at obtaining a set of schedules that can be considered efficient with respect to a performance measure and at the same time different enough from one another to enable flexibility in the final choice. We formalize this new problem and suggest to address it with a multimodal optimization approach. Niching considerations are discussed for common scheduling problems. Through the flexibility induced with this approach, the additional considerations can be taken into account in a second step, which allows decision-makers to select an appropriate schedule among a set of sound schedules (in contrast to common optimization approaches, where usually a single solution is obtained and it is final). The proposed two-step approach can be used to handle a wide range of underlying scheduling problems. To show its potential and benefits we illustrate the framework on a set of hybrid flow shop instances that have been previously studied in the literature. We develop a multimodal genetic algorithm that employs an adapted version of the restricted tournament selection for niching purposes in the first step. The second step takes into account additional concerns of the decision-maker related to the ability of the schedules to absorb the negative effects due to random machine breakdowns. Our computational experiments indicate that the proposed framework is capable of generating numerous high-performance (mostly optimal) schedules. Additionally, our computational results demonstrate that the proposed framework provides the decision-maker a high flexibility in dealing with subsequent side concerns, since there are drastic differences in the capabilities of the efficient solutions found in Step 1 to absorb the negative impacts of machine breakdowns. (C) 2013 Elsevier Ltd. All rights reserved.
  • Conference Object
    Normal Mixture Model-Based Clustering of Data Using Genetic Algorithm
    (Springer International Publishing AG, 2020) Gogebakan, Maruf; Erol, Hamza
    In this study, a new algorithm was developed for clustering multivariate big data. Normal mixture distributions are used to determine the partitions of variables. Normal mixture models obtained from the partitions of variables are generated using Genetic Algorithms (GA). Each partition in the variables corresponds to a clustering center in the normal mixture model. The best model that fits the data structure from normal mixture models is obtained by using the information criteria obtained from normal mixture distributions.
  • Article
    Citation - WoS: 34
    Citation - Scopus: 38
    Green Building Envelope Designs in Different Climate and Seismic Zones: Multi-Objective ANN-Based Genetic Algorithm
    (Elsevier, 2022-10) Himmetoglu, Salih; Delice, Yilmaz; Aydogan, Emel Kizilkaya; Uzal, Burak; Kızılkaya Aydoğan, Emel
    In recent years, the major component of green building designs adopted by governments in order to reduce CO2 emissions as well as energy consumption is the green building envelope. The green envelope has the most important share in terms of thermal energy consumption, environment, and indoor comfort criteria. Determining the most suitable building envelope combination in the building life cycle is an important problem for designers. This study presents a new multi-objective approach that determines the most suitable green envelope designs for the buildings in different climate and earthquake zones, taking into account CO2 emissions, heating/cooling energy consumption, and material cost in terms of life cycle cost analysis. To this end, EnergyPlus building performance simulation program, artificial neural network (ANN), and genetic algorithm are used together. After the heating and cooling energy consumption, CO2 emissions, and material cost values are obtained for a certain number of the envelope alternatives with the EnergyPlus, ANN models that learn the working mechanism of EnergyPlus are trained according to these values. An ANN-based genetic algorithm procedure is developed to search the whole envelope alternative space by using the trained ANN models with EnergyPlus. The proposed approach allows searching in a very short time the whole alternative space, which is almost impossible to scan with EnergyPlus by reducing the time spent and the number of alternatives required for the design and simulation processes of the green building envelope. The proposed approach is performed for a design-stage city hospital structure in Turkey. Window type, the internal/external plaster, wall, and insulation materials along with the thicknesses of these materials, which consist of 46 different variables, are determined as envelope attributes for four different climate and seismic zones. The green building envelope designs obtained with the proposed approach are entered into EnergyPlus and the consistency of the results is compared. ANN models with an average accuracy of over 97% are developed. Without the CO2 emission cost in the life cycle cost, the mean absolute percent error (MAPE) values for each region are 0.67%, 0.6%, 0.58%, and 1.78%, respectively. With the CO2 emission cost in life cycle cost, the MAPE values for each region are 0.96%, 0.88%, 0.86%, and 0.43%, respectively. According to the obtained results, there is a consistency of over 99% between EnergyPlus and the proposed approach.
  • Article
    Citation - Scopus: 100
    An Optimally Configured and Improved Deep Belief Network (OCI-DBN) Approach for Heart Disease Prediction Based on Ruzzo-Tompa and Stacked Genetic Algorithm
    (Institute of Electrical and Electronics Engineers Inc., 2020) Ali, Syed Arslan; Raza, Basit; Malik, Ahmad Kamran Kamran; Shahid, Ahmad Raza; Faheem, Muhammed Yasir; Alquhayz, Hani Ali; Kumar, Y. J.
    A rapid increase in heart disease has occurred in recent years, which might be the result of unhealthy food, mental stress, genetic issues, and a sedentary lifestyle. There are many advanced automated diagnosis systems for heart disease prediction proposed in recent studies, but most of them focus only on feature preprocessing, some focus on feature selection, and some only on improving the predictive accuracy. In this study, we focus on every aspect that may have an influence on the final performance of the system, i.e., to avoid overfitting and underfitting problems or to solve network configuration issues and optimization problems. We introduce an optimally configured and improved deep belief network named OCI-DBN to solve these problems and improve the performance of the system. We used the Ruzzo-Tompa approach to remove those features that are not contributing enough to improve system performance. To find an optimal network configuration, we proposed a stacked genetic algorithm that stacks two genetic algorithms to give an optimally configured DBN. An analysis of a RBM and DBN trained is performed to give an insight how the system works. Six metrics were used to evaluate the proposed method, including accuracy, sensitivity, specificity, precision, F1 score, and Matthew's correlation coefficient. The experimental results are compared with other state-of-the-art methods, and OCI-DBN shows a better performance. The validation results assure that the proposed method can provide reliable recommendations to heart disease patients by improving the accuracy of heart disease predictions by up to 94.61%. © 2020 Elsevier B.V., All rights reserved.
  • Article
    Citation - WoS: 13
    Citation - Scopus: 15
    4D-QSAR Investigation and Pharmacophore Identification of Pyrrolo[2,1-C][1,4]Benzodiazepines Using Electron Conformational-Genetic Algorithm Method
    (Taylor & Francis Ltd, 2016-04-02) Ozalp, A.; Yavuz, S. C.; Sabanci, N.; Copur, F.; Kokbudak, Z.; Saripinar, E.
    In this paper, we present the results of pharmacophore identification and bioactivity prediction for pyrrolo[2,1-c][1,4]benzodiazepine derivatives using the electron conformational-genetic algorithm (EC-GA) method as 4D-QSAR analysis. Using the data obtained from quantum chemical calculations at PM3/HF level, the electron conformational matrices of congruity (ECMC) were constructed by EMRE software. The ECMC of the lowest energy conformer of the compound with the highest activity was chosen as the template and compared with the ECMCs of the lowest energy conformer of the other compounds within given tolerances to reveal the electron conformational submatrix of activity (ECSA, i.e. pharmacophore) by ECSP software. A descriptor pool was generated taking into account the obtained pharmacophore. To predict the theoretical activity and select the best subset of variables affecting bioactivities, the nonlinear least square regression method and genetic algorithm were performed. For four types of activity including the GI(50), TGI, LC50 and IC50 of the pyrrolo[2,1-c][1,4] benzodiazepine series, the r(train)(2), r(test)(2) and q(2) values were 0.858, 0.810, 0.771; 0.853, 0.848, 0.787; 0.703, 0.787, 0.600; and 0.776, 0.722, 0.687, respectively.