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## Hybrid Job Scheduling Algorithm for Cloud Computing Environment

### Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014 (2014-01-01) 303: 43-52 , January 01, 2014

In this paper with the aid of genetic algorithm and fuzzy theory, we present a hybrid job scheduling approach, which considers the load balancing of the system and reduces total execution time and execution cost. We try to modify the standard Genetic algorithm and to reduce the iteration of creating population with the aid of fuzzy theory. The main goal of this research is to assign the jobs to the resources with considering the VM MIPS and length of jobs. The new algorithm assigns the jobs to the resources with considering the job length and resources capacities. We evaluate the performance of our approach with some famous cloud scheduling models. The results of the experiments show the efficiency of the proposed approach in term of execution time, execution cost and average Degree of Imbalance (DI).

## A Pi-Sigma Higher Order Neural Network for Stock Index Forecasting

### Computational Intelligence in Data Mining - Volume 2 (2015-01-01) 32: 311-319 , January 01, 2015

Multilayer perceptron (MLP) has been found to be most frequently used model for stock market forecasting. MLP is characterized with black-box in nature and lack of providing a formal method of deriving ultimate structure of the model. Higher order neural network (HONN) has the ability to expand the input representation space, perform high learning capabilities that require less memory in terms of weights and nodes and have been utilized in many complex data mining problems. To capture the extreme volatility, nonlinearity and uncertainty associated with stock data, this paper considered a HONN, called Pi-Sigma Neural Network (PSNN), for prediction of closing prices of five real stock markets. The tunable weights are optimized by Gradient Descent (GD) and a global search technique, Genetic Algorithm (GA). The model proves its superiority when trained with GA in terms of Average Percentage of Errors (APE).

## Optimization of soil moisture sensor placement for a PV-powered drip irrigation system using a genetic algorithm and artificial neural network

### Electrical Engineering (2017-03-01) 99: 407-419 , March 01, 2017

The efficiency and installation costs of solar-powered drip irrigation systems depend on not only the efficiencies of the electrical motor, its driver, and the pump, but also the efficient usage of irrigation water. In this study, the initial installation costs and energy consumption of photovoltaic irrigation systems were decreased by obtaining the soil moisture level as a reference for optimizing energy and water consumption in a solar-powered drip irrigation system. The data from 15 moisture sensors placed in the area covered by the system were collected by a central unit using radio transmission. The soil moisture was estimated via an artificial neural network with the data obtained for $$6\,\hbox {m} \times 6\,\hbox {m}$$ micro-regions. Next, the locations of the moisture sensors in the area were optimized using a genetic algorithm to provide the optimum energy and water consumption in the system. Subsequently, the drip irrigation was controlled using moisture data from only five sensors located at the best points, as determined by the genetic algorithm. The obtained experimental results indicated that the moisture rate at the end of the period of irrigation using the system developed was more homogeneous than that of traditional irrigation systems for each micro-region using only five soil moisture sensors in a non-homogeneous area. Thus, daily energy and water consumption were decreased by 32 %, while the moisture rate in the soil was maintained within the desired range.

## Optimization of vendor managed inventory of multiproduct EPQ model with multiple constraints using genetic algorithm

### The International Journal of Advanced Manufacturing Technology (2014-03-01) 71: 365-376 , March 01, 2014

The aim of this paper is to investigate the vendor managed inventory (VMI) problem of a single-vendor single-buyer supply chain system, in which the vendor is responsible to manage the buyer’s inventory. To include an extended applicability in real-world environments, the multiproduct economic production quantity model with backordering under three constraints of storage capacity, number of orders, and available budget is considered. The nonlinear programming model of the problem is first developed to determine the near optimal order quantities along with the maximum backorder levels of the products in a cycle such that the total VMI inventory cost of the system is minimized. Then, a genetic algorithm (GA) based heuristic is proposed to solve the model. Numerical examples are given to both demonstrate the applicability of the proposed methodology and to fine tune the GA parameters. At the end, the performance of the proposed GA is compared to the one of the LINGO software using different problem sizes. The results of the comparison study show that, while the solutions do not differ significantly, the proposed GA reaches near optimum solutions in significantly less amount of CPU time.

## Identification of Deleterious SNPs in TACR1 Gene Using Genetic Algorithm

### Computational Intelligence Techniques for Comparative Genomics (2015-01-01) , January 01, 2015

Bioinformatics is a specific research and development area. The purpose of bioinformatics mainly deals with data mining and the relationships and patterns in large databases to provide useful information analysis and diagnosis. Single nucleotide polymorphisms (SNP) are one of the major causes of genetic diseases. Identification of disease-causing SNPs can identify better disease diagnosis. Hence, the present study aims at the identification of deleterious SNPs in TACR1 gene. Developing an algorithm plays a vital role in computational intelligence techniques. In this paper, a genetic algorithm (GA) approach is to develop rules and it is presented. The importance of the accuracy, sensitivity, specificity, and comprehensibility of the rules is simplified for the implementation of a GA. The outline of encoding and genetic operators and fitness function of GA are discussed. GA is using to identify deleterious or damaged SNPs.

## Estimation of Population Pharmacokinetic Parameters Using a Genetic Algorithm

### Nature-Inspired Design of Hybrid Intelligent Systems (2017-01-01) 667: 493-503 , January 01, 2017

Population pharmacokinetics (PopPK) models are used to characterize the behavior of a drug in a particular population. Construction of PopPK models requires the estimation of optimal PopPK parameters, which is a challenging task due to the characteristics of the PopPK database. Several estimation algorithms have been proposed for estimating PopPK parameters; however, the majority of these methods are based on maximum likelihood estimation methods that optimize the probability of observing data, given a model that requires the systematic computation of the first and second derivate of a multivariate likelihood function. This work presents a genetic algorithm for obtaining optimal PopPK parameters by directly optimizing the multivariate likelihood function avoiding the computation of the first and second derivate of the likelihood function.

## Cell formation in the presence of reconfigurable machines

### The International Journal of Advanced Manufacturing Technology (2007-09-01) 34: 335-345 , September 01, 2007

In this research, an approach is made to design machine cells using modular machines to achieve certain characteristics of reconfigurable manufacturing. Each machine considered in the model consists of some basic and auxiliary machine modules. By changing the auxiliary modules, different operations can be performed on the machines. A similarity measure among machines based on production flow information and auxiliary module requirement is developed. Machine cells are identified using a multi-objective evolutionary genetic algorithm for a set of parts with parameters such as the volumes of production, alternative operation-based process plans, etc. The two objectives considered are the minimization of inter-cell movement and the total changes in auxiliary modules for the given production horizon. The resulting cell configuration is simulated to find the exact inter-cellular movements and the total number of module changes to validate the heuristic. An illustrative problem and experimental results are given.

## Evolutionary Algorithm Based Techniques to Handle Big Data

### Techniques and Environments for Big Data Analysis (2016-01-01) 17: 113-158 , January 01, 2016

Big data is an all-encompassing term for any collection of data sets so large and complex that it becomes difficult to process them using traditional data processing applications. So Data mining, which has as a goal to extract knowledge from large databases, has become a challenge for this large and complex data set [52]. Other challenges in handling Big data include analysis, capture, curation, search, sharing, storage, transfer, visualization, and privacy violations. To extract and handle this large amount of knowledge, a database may be considered as a large search space, and a mining algorithm as a search strategy. In general, a search space consists of an enormous number of elements, making an exhaustive search infeasible. Therefore, efficient search strategies are of vital importance. Search strategies based on Evolutionary algorithms have been applied successfully in a wide range of applications. In this chapter, we discuss about Big data and limitations of standard algorithms handling them, Evolutionary Algorithm and their advantages in handling Big data, Commonly used Evolutionary algorithm—Genetic Algorithm and the various application areas where genetic Algorithm plays evolutionary role in the large and complex search space.

## Intelligent Prediction of Properties of Wheat Grains Using Soft Computing Algorithms

### Advanced Computing and Communication Technologies (2016-01-01) 452: 79-87 , January 01, 2016

In this paper, chemical properties of wheat dough are predicted using different soft computing tools. Here, back-propagation and genetic algorithm techniques are used to predict the parameters and a comparative study is made. Wheat grains are stored at controlled environmental conditions. The content of fat, moisture, ash, time and temperature are considered as inputs whereas protein and carbohydrate contents are chosen as outputs. The prediction algorithm is developed using back-propagation algorithm, number of layers are optimized and mean square errors are minimized. The errors are further reduced by optimizing the weights using Genetic Algorithm and again the outputs are obtained. The error between predicted and actual outputs is calculated. It has been observed that with back-propagation along GA model algorithm, errors are less compared to the simple back-propagation algorithm. Hence, the given network can be considered as beneficial as it predicts more accurately. Numerical results along with discussions are presented.

## RF integrated inductor modeling and its application to optimization-based design

### Analog Integrated Circuits and Signal Processing (2012-10-01) 73: 47-55 , October 01, 2012

In this paper an optimization-based approach for the design of RF integrated inductors is addressed. For the characterisation of the inductor behaviour the double π-model is used. The use of this model is twofold. On one hand it enables the generation of the inductor characterisation in a few seconds. On the other hand its integration into the optimization procedure is straightforward. For the evaluation of the model element values analytical expressions based on technology parameters as well as on the device geometric characteristics are used. The use of a technology-based methodology for the evaluation of the model parameters grants the adaptability of the model to any technology. The inductor analytical characterization is integrated into an optimization-based tool for the automatic design of RF integrated inductors. This tool uses a modified genetic algorithm (MGA) optimization procedure, which has proved its validation in previous work. Due to the design parameter constraints nature as well as the topology constraints, discrete variables optimization techniques are used. The accuracy of the results is checked against a non-commercial software.