Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction

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A Tutorial on Clique Problems in Communications and Signal Processing

Since its first use by Euler on the problem of the seven bridges of K\"onigsberg, graph theory has shown excellent abilities in solving and unveiling the properties of multiple discrete optimization problems. The study of the structure of some integer programs reveals equivalence with graph theory problems making a large body of the literature readily available for solving and characterizing the complexity of these problems. This tutorial presents a framework for utilizing a particular graph theory problem, known as the clique problem, for solving communications and signal processing problems. In particular, the paper aims to illustrate the structural properties of integer programs that can be formulated as clique problems through multiple examples in communications and signal processing. To that end, the first part of the tutorial provides various optimal and heuristic solutions for the maximum clique, maximum weight clique, and $k$-clique problems. The tutorial, further, illustrates the use of the clique formulation through numerous contemporary examples in communications and signal processing, mainly in maximum access for non-orthogonal multiple access networks, throughput maximization using index and instantly decodable network coding, collision-free radio frequency identification networks, and resource allocation in cloud-radio access networks. Finally, the tutorial sheds light on the recent advances of such applications, and provides technical insights on ways of dealing with mixed discrete-continuous optimization problems

Computational Modelling Approach for the Optimization of Apple Juice Clarification using Immobilized Pectinase and Xylanase Enzymes

Apple juice is typically marketed as a clear juice, and hence enzymatic treatments are common practices in juice industry. However, enzymatic treatments have been shown to face some challenges when process efficiency, and cost effectiveness are concerned. Therefore, it is necessary to optimize the enzymatic treatment process to maximize efficiency, and reuse enzymes to minimize the overall cost via immobilization. In this context, the present work features the immobilization of pectinase and xylanase from M. hiemalis on genipin-activated alginate beads, with subsequent evaluation of its efficacy in apple juice clarification. A central composite rotatable design (CCRD), coupled with artificial neural network (ANN) for modeling and optimization was used to design the experiments. Deploying a coupling time up to 120 min, and an agitation rate of 213 rpm (pectinase) - 250 rpm (xylanase), a maximum fractional enzyme activity recovered was observed to be about 81–83% for both enzymes. Optimum enzyme loading and genipin concentration were found to be 50 U/ml and 12% (w/v), respectively. The immobilized enzyme preparations were suitable for up to 5 repeated process cycles, losing about 45% (pectinase) - 49% (xylanase) of their initial activity during this time. The maximum clarity of apple juice (%T660, 84%) was achieved at 100 min when pectinase (50 U/ml of juice) and xylanase (20 U/ml of juice) were used in combination at 57 °C. The immobilized enzymes are of industrial relevance in terms of biocompatibility, recoverability, and operational-storage stability

Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection Within Fruit Juice Classification

Research article[Abstract] Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected

A Genetic Algorithm-Based Feature Selection

This article details the exploration and application of Genetic Algorithm (GA) for feature selection. Particularly a binary GA was used for dimensionality reduction to enhance the performance of the concerned classifiers. In this work, hundred (100) features were extracted from set of images found in the Flavia dataset (a publicly available dataset). The extracted features are Zernike Moments (ZM), Fourier Descriptors (FD), Lengendre Moments (LM), Hu 7 Moments (Hu7M), Texture Properties (TP) and Geometrical Properties (GP). The main contributions of this article are (1) detailed documentation of the GA Toolbox in MATLAB and (2) the development of a GA-based feature selector using a novel fitness function (kNN-based classification error) which enabled the GA to obtain a combinatorial set of feature giving rise to optimal accuracy. The results obtained were compared with various feature selectors from WEKA software and obtained better results in many ways than WEKA feature selectors in terms of classification accuracy

A genetic Algorithm-Based feature selection

This article details the exploration and application of Genetic Algorithm (GA) for feature selection. Particularly a binary GA was used for dimensionality reduction to enhance the performance of the concerned classifiers. In this work, hundred (100) features were extracted from set of images found in the Flavia dataset (a publicly available dataset). The extracted features are Zernike Moments (ZM), Fourier Descriptors (FD), Lengendre Moments (LM), Hu 7 Moments (Hu7M), Texture Properties (TP) and Geometrical Properties (GP). The main contributions of this article are (1) detailed documentation of the GA Toolbox in MATLAB and (2) the development of a GA-based feature selector using a novel fitness function (kNN-based classification error) which enabled the GA to obtain a combinatorial set of feature giving rise to optimal accuracy. The results obtained were compared with various feature selectors from WEKA software and obtained better results in many ways than WEKA feature selectors in terms of classification accuracy

Solución de un modelo de arreglos fotovoltaicos serie-paralelo utilizando algoritmos de optimización global

Models of series-parallel (SP) photovoltaic (PV) arrays focus on the system of nonlinear equations that represents the array’s electrical behavior. The solution of the system of nonlinear equations can be posed as an optimization problem and solved with different methods; however, the models do not formulate the optimization problem and do not evaluate different optimization algorithms for its solution. This paper proposes a solution, using global optimization algorithms, of the mathematical model that describes the electrical behavior of a SP generator, operating under uniform and partial shading conditions. Such a model is constructed by dividing the generator into strings and representing each module in the string with the single-diode model. Consequently, for each string a system of nonlinear equations is build applying the Kirchhoff’s laws, where the unknowns are the modules’ voltages. The solution of the resulting nonlinear equation system is posed as an optimization problem, where the objective function is defined as the sum of the squared of each nonlinear equation. Minimum and maximum values of each voltage are defined from the datasheet information of the modules and bypass diodes. As a demonstrative example, we arbitrarily select two well-known algorithms to solve this problem: Genetic Algorithms and Particle Swarm Optimization. Simulation results show that both algorithms solve the optimization problem and allow the reproduction of the generator’s characteristic curves. Moreover, the results also indicate that the optimization problem is correctly defined, which opens the possibility explore other optimization algorithms to reduce the computation time.Los modelos de arreglos fotovoltaicos (FV) en serie-paralelo (SP) se enfocan en el sistema de ecuaciones no lineales que represental comportamiento eléctrico del arreglo. La solución del sitemas de ecuaciones se puede plantear como un problema de optimización y resolverse con diferentes métodos; sin embargo, los modelos no formulan el problema de optimización y no evaluan diferentes algoritmos de optimización para su solución. Este artículo propone una solución, utilizando algoritmos de optimización global, del modelo matemático que describe el comportamiento eléctrico de un generador fotovoltaico en serie-paralelo, que opera bajo condiciones uniformes y de sombreados parciales. Dicho modelo se construye dividiendo el generador en cadenas y representando cada módulo en la cadena con el modelo de diodo-único. En consecuencia, para cada cadena se construye un sistema de ecuaciones no lineales aplicando las leyes de Kirchhoff, en donde las incógnitas son los voltajes de los módulos. La solución del sistema de ecuaciones no lineales resultante se plantea como un problema de optimización, donde la función objetivo se define como la suma del cuadrado de cada ecuación no lineal. Los valores mínimos y máximos de cada voltaje se definen a partir de la información de la hoja de datos de los módulos y de los diodos de derivación. Como ejemplo demostrativo, se seleccionaron arbitrariamente dos algoritmos bien conocidos para resolver este problema: Algoritmos Genéticos y Optimización por Enjambre de Partículas. Los resultados de simulación muestran que los dos algoritmos ambos algoritmos resuelven el problema de optimización y permiten la reproducción de las curvas características del generador. Adicionalmente, los resultados también indican que el problema de optimización se definió correctamente, lo cual abre la posibilidad de explorar otros algoritmos de optimización para reducir el tiempo de cómputo

A Survey on Feature Selection Algorithms

One major component of machine learning is feature analysis which comprises of mainly two processes: feature selection and feature extraction. Due to its applications in several areas including data mining, soft computing and big data analysis, feature selection has got a reasonable importance. This paper presents an introductory concept of feature selection with various inherent approaches. The paper surveys historic developments reported in feature selection with supervised and unsupervised methods. The recent developments with the state of the art in the on-going feature selection algorithms have also been summarized in the paper including their hybridizations. DOI: 10.17762/ijritcc2321-8169.16043

Solución de un modelo de arreglos fotovoltaicos serie-paralelo utilizando algoritmos de optimización global

Models of series-parallel (SP) photovoltaic (PV) arrays focus on the system of nonlinear equations that represents the array’s electrical behavior. The solution of the system of nonlinear equations can be posed as an optimization problem and solved with different methods; however, the models do not formulate the optimization problem and do not evaluate different optimization algorithms for its solution. This paper proposes a solution, using global optimization algorithms, of the mathematical model that describes the electrical behavior of a SP generator, operating under uniform and partial shading conditions. Such a model is constructed by dividing the generator into strings and representing each module in the string with the single-diode model. Consequently, for each string a system of nonlinear equations is build applying the Kirchhoff’s laws, where the unknowns are the modules’ voltages. The solution of the resulting nonlinear equation system is posed as an optimization problem, where the objective function is defined as the sum of the squared of each nonlinear equation. Minimum and maximum values of each voltage are defined from the datasheet information of the modules and bypass diodes. As a demonstrative example, we arbitrarily select two well-known algorithms to solve this problem: Genetic Algorithms and Particle Swarm Optimization. Simulation results show that both algorithms solve the optimization problem and allow the reproduction of the generator’s characteristic curves. Moreover, the results also indicate that the optimization problem is correctly defined, which opens the possibility explore other optimization algorithms to reduce the computation time.Los modelos de arreglos fotovoltaicos (FV) en serie-paralelo (SP) se enfocan en el sistema de ecuaciones no lineales que represental comportamiento eléctrico del arreglo. La solución del sitemas de ecuaciones se puede plantear como un problema de optimización y resolverse con diferentes métodos; sin embargo, los modelos no formulan el problema de optimización y no evaluan diferentes algoritmos de optimización para su solución. Este artículo propone una solución, utilizando algoritmos de optimización global, del modelo matemático que describe el comportamiento eléctrico de un generador fotovoltaico en serie-paralelo, que opera bajo condiciones uniformes y de sombreados parciales. Dicho modelo se construye dividiendo el generador en cadenas y representando cada módulo en la cadena con el modelo de diodo-único. En consecuencia, para cada cadena se construye un sistema de ecuaciones no lineales aplicando las leyes de Kirchhoff, en donde las incógnitas son los voltajes de los módulos. La solución del sistema de ecuaciones no lineales resultante se plantea como un problema de optimización, donde la función objetivo se define como la suma del cuadrado de cada ecuación no lineal. Los valores mínimos y máximos de cada voltaje se definen a partir de la información de la hoja de datos de los módulos y de los diodos de derivación. Como ejemplo demostrativo, se seleccionaron arbitrariamente dos algoritmos bien conocidos para resolver este problema: Algoritmos Genéticos y Optimización por Enjambre de Partículas. Los resultados de simulación muestran que los dos algoritmos ambos algoritmos resuelven el problema de optimización y permiten la reproducción de las curvas características del generador. Adicionalmente, los resultados también indican que el problema de optimización se definió correctamente, lo cual abre la posibilidad de explorar otros algoritmos de optimización para reducir el tiempo de cómputo