Population Size In Genetic Algorithm Research Articles

Mechanical properties of cemented tailings and waste-rock backfill (CTWB) materials: Laboratory tests and deep learning modeling

Cemented tailings and waste-rock backfill (CTWB) is an effective way to solve the problem of mine solid waste. Waste-rock content is an important factor affecting UCS, but fully understanding the mechanism of waste-rock action on UCS would require extensive indoor testing, which would be tedious and expensive. Therefore, the mechanical properties of CTWB were investigated using a combination of laboratory testing and deep learning. Long short-term memory (LSTM) network prediction model based on genetic algorithm (GA) optimization was developed with cement content (290 ∼ 330 kg/m3), solid content (75 ∼ 81%), waste-rock content (40 ∼ 70%), and curing age as input variables and unconfined compressive strength (UCS) as output variables. Among them, the population size of GA was set to 40, and the crossover rate and mutation rate were set to 0.75 and 0.01, respectively. The study evaluated the models using RMSE, R2, and MAE and plotted normalized Taylor diagrams for different prediction models. The results show that the GA algorithm is effective for the parameter rectification of the LSTM model. The representative GA-LSTM model has an R2 of 0.9956 in the training set and an R2 of 0.9763 in the test set, indicating a good prediction effect. Deep learning models exhibit higher accuracy than machine learning models. Compared with GA-SVM, LSTM, and PSO-LSTM models, R2 increases from 0.9483, 0.8474, and 0.9611 to 0.9968, respectively, indicating that the model has good robustness and generalization ability. Moreover, the correlation analysis shows that the UCS reaches the maximum value when the waste rock content is 50%, and the hydration products can effectively fill the pores and form a more stable internal structure. The research results will provide technical support for safe, clean, and efficient recovery.

Design of marine propellers with prescribed and optimal spanwise circulation distributions based on genetic algorithms and neural network

Lifting-surface design methods are proposed for marine propellers based on genetic algorithms (GAs) and a two-layer back-propagation neural network (BPNN). For propellers with prescribed spanwise circulation distributions, the design problem is solved as GA-based optimization of camber surface geometry and pitch distribution which produce a circulation distribution to best fit the prescribed one. An in-house code based on the vortex lattice method (VLM) is employed to simulate circulation distribution and hydrodynamic performance of the propeller. Computer codes are developed in this research based on existing genetic algorithms, with a measure devised to accelerate convergence and improve the quality of solution. To optimize the spanwise circulation distribution, GAs are utilized again to explore the BPNN model established via the MATLAB toolbox and the in-house VLM code. Then the optimal circulation distribution is taken as the prescribed one for the GA-based design method mentioned above. Numerical tests are conducted to determine proper ranges of modeling parameters, such as the population size for GAs and the number of vortex lattices, and to assess the performance of the BPNN established. To numerically validate the proposed methods, viscous-flow simulation by solving the Reynolds-averaged Navier-Stokes equations is carried out for the propeller designed by using both methods mentioned above. The predicted pressure distributions over blade surfaces correlate consistently with the circulation distributions used to design the propeller, thus indicating that present design methods are effective and reasonably accurate.

Performance Evaluations for OpenMP Accelerated Training Of Separable Image Filter

One of the widespread image processing applications is image filtering with two dimensional convolution. Determining the weights of image filters are of importance for the success of filtering operation. Heuristic algorithms such as genetic algorithms provide an efficient way of training these types of filters. Due to the high computational cost of repetitive image filtering operations, this process may take hours to implement using single core computing. OpenMP (Open Multi Processing) provides an efficient library for utilizing the computing power of multicore processors. In this study, OpenMP accelerated training of separable filters that are a subclass of convolution filters has been implemented based on genetic algorithms. Comparative speed-up results for various sizes of images using various sizes of filtering kernels were presented. Also the effect of population size of genetic algorithm and the number of working cores have been investigated.

Distribution network reconfiguration using a genetic algorithm with varying population size

Genetic algorithm (GA) has been shown to be an effective way to solve the complex combinatorial, and constrained non-linear mixed integer optimization problem of distribution network reconfiguration. Extensive work has been done in the literature to efficiently apply GA to the reconfiguration problem. Nonetheless, to date, all the previous work in this area have presupposed that the GA population size should remain constant throughout the evolution of the search process. In this paper, we propose the application of a genetic algorithm with variable population size (GAVAPS) to the reconfiguration problem. We demonstrate that allowing the population size to adaptively grow and shrink according to the status of the GA search can allow for a more efficient solution, compared to standard genetic algorithm.

Prediction of contraction scour using ANN and GA

This paper emphasizes on the application of soft computing tools such as artificial neural network (ANN) and genetic algorithm (GA) in the prediction of scour depth within channel contractions. The experimental data of earlier investigators are used in developing the models and ANN and GA Toolboxes of MATLAB software are utilized for the purpose. The multilayered perceptron (MLP) neural networks with feed-forward back-propagation training algorithms were designed to predict the scour depth. The mean squared error and correlation coefficient are used to check the performance of networks. It is found that the ANN architecture 4-16-1 having trained with Levenberg-Marquardt ‘trainlm’ function had best performance having mean squared error of 0.001 and correlation coefficient of 0.998. In addition, the suitability of ‘trainlm’ method over other training methods is also discussed. The scour depths predicted by ANN model were compared with those computed by the two analytical models (with and without sidewall correction for contracted zone) and an empirical model proposed by Dey and Raikar [1]. In addition, heuristic search technique called genetic algorithm is used to develop the predictor for maximum scour depth within channel contraction. The population size for GA was 500 members with total generations of 1000, crossover fraction of 0.8 and Gaussian operator for mutation. It is promising to observe that the GA model predicts the maximum scour depth equally well as that of empirical model of Dey and Raikar [1]. Hence, both ANN and GA models can be satisfactorily used to predict the scour depth within channel contractions.

Adaptive Population Sizing Genetic Algorithm Assisted Maximum Likelihood Detection of OFDM Symbols in the Presence of Nonlinear Distortions

This paper presents Adaptive Population Sizing Genetic Algorithm (AGA) assisted Maximum Likelihood (ML) estimation of Orthogonal Frequency Division Multiplexing (OFDM) symbols in the presence of Nonlinear Distortions. The proposed algorithm is simulated in MATLAB and compared with existing estimation algorithms such as iterative DAR, decision feedback clipping removal, iteration decoder, Genetic Algorithm (GA) assisted ML estimation and theoretical ML estimation. Simulation results proved that the performance of the proposed AGA assisted ML estimation algorithm is superior compared with the existing estimation algorithms. Further the computational complexity of GA assisted ML estimation increases with increase in number of generations or/and size of population, in the proposed AGA assisted ML estimation algorithm the population size is adaptive and depends on the best fitness. The population size in GA assisted ML estimation is fixed and sufficiently higher size of population is taken to ensure good performance of the algorithm but in proposed AGA assisted ML estimation algorithm the size of population changes as per requirement in an adaptive manner thus reducing the complexity of the algorithm.

Azadirachtin Interacts with Retinoic Acid Receptors and Inhibits Retinoic Acid-mediated Biological Responses

Considering the role of retinoids in regulation of more than 500 genes involved in cell cycle and growth arrest, a detailed understanding of the mechanism and its regulation is useful for therapy. The extract of the medicinal plant Neem (Azadirachta indica) is used against several ailments especially for anti-inflammatory, anti-itching, spermicidal, anticancer, and insecticidal activities. In this report we prove the detailed mechanism on the regulation of retinoic acid-mediated cell signaling by azadirachtin, active components of neem extract. Azadirachtin repressed all trans-retinoic acid (ATRA)-mediated nuclear transcription factor κB (NF-κB) activation, not the DNA binding but the NF-κB-dependent gene expression. It did not inhibit IκBα degradation, IκBα kinase activity, or p65 phosphorylation and its nuclear translocation but inhibited NF-κB-dependent reporter gene expression. Azadirachtin inhibited TRAF6-mediated, but not TRAF2-mediated NF-κB activation. It inhibited ATRA-induced Sp1 and CREB (cAMP-response element-binding protein) DNA binding. Azadirachtin inhibited ATRA binding with retinoid receptors, which is supported by biochemical and in silico evidences. Azadirachtin showed strong interaction with retinoid receptors. It suppressed ATRA-mediated removal of retinoid receptors, bound with DNA by inhibiting ATRA binding to its receptors. Overall, our data suggest that azadirachtin interacts with retinoic acid receptors and suppresses ATRA binding, inhibits falling off the receptors, and activates transcription factors like CREB, Sp1, NF-κB, etc. Thus, azadirachtin exerts anti-inflammatory and anti-metastatic responses by a novel pathway that would be beneficial for further anti-inflammatory and anti-cancer therapies.

Statistical inverse analysis based on genetic algorithm and principal component analysis: Method and developments using synthetic data

AbstractThis study concerns the identification of parameters of soil constitutive models from geotechnical measurements by inverse analysis. To deal with the non‐uniqueness of the solution, the inverse analysis is based on a genetic algorithm (GA) optimization process. For a given uncertainty on the measurements, the GA identifies a set of solutions. A statistical method based on a principal component analysis (PCA) is, then, proposed to evaluate the representativeness of this set. It is shown that this representativeness is controlled by the GA population size for which an optimal value can be defined. The PCA also gives a first‐order approximation of the solution set of the inverse problem as an ellipsoid. These developments are first made on a synthetic excavation problem and on a pressuremeter test. Some experimental applications are, then, studied in a companion paper, to show the reliability of the method. Copyright © 2009 John Wiley & Sons, Ltd.