Genetic Algorithms

In Industrial environments, huge amount of data is being generated which in turn collected indatabase anddata warehouses from all involved areas such as planning, process design, materials, assembly, production, quality, process control, scheduling, fault detection,shutdown, customer relation management, and so on. Data Mining has become auseful tool for knowledge acquisition for industrial process of Iron and steel making. Due to the rapid growth in Data Mining, various industries started using data mining technology to search the hidden patterns, which might further be used to the system with the new knowledge which might design new models to enhance the production quality, productivity optimum cost and maintenance etc. The continuous improvement of all steel production process regarding the avoidance of quality deficiencies and the related improvement of production yield is an essential task of steel producer. Therefore, zero defect strategy is popular today and to maintain it several quality assurance techniques areused. The present report explains the methods of data mining and describes its application in the industrial environment and especially, in the steel industry.

The foremost objective of the present research project consists developing an Intelligent Robotic System (SIR for its name in Spanish, Sistema Inteligente Robótico) that solves an unknown jigsaw puzzle in a reduced amount of time. To fulfill the objective, SIR applies pattern recognition techniques as edge and feature detection in conjunction with Genetic Algorithms. Many authors have addressed the jigsaw puzzle problem. By comparing their work, the NP-Complete nature of the problem appeared as a common denominator on them. SIR relies on those experiences, results, conclusions and observations as a working base for a new approach to the problem. This approach involves a conversion of the puzzle model to a Graph Theory model. The study of this model plus the inclusion of a different analogy and solution approach is the main contribution of this work. It describes the theoretical and practical frameworks, current state of the project and future work.

Page 1. A Parallel Genetic Algorithm for Rule Discovery in Large Databases Dieferson Luis Alves de Araujo' , Heitor S. Lopes', Alex A. Freitas2 CEFET-PR - Centro Federal de EducaGBo Tecnol6gica do Paranh CPGEI - Curso ...

In this paper a method based on combining adaptive neuro fuzzy inference systems (ANFISs) and genetic algorithm (GA) is applied for design and optimization of a circularly polarized microstrip antenna for L1 frequency band of GPS. In design process, trained ANFISs are used for estimating return loss and axial ratio. In optimization process, a proper objective function is defined and minimized with GA in order to obtain optimum physical parameters. The optimization method is much faster than conventional optimization methods. Both simulation and measurement results confirm the accuracy and efficiency of the method.

This paper proposes a new niching technique for Distributed Genetic Algo-rithm (DGA) . In DGA quick evolution takes place because of independent evolution of subpopulation. To improve performance of DGA care need to be taken for distribution of population in to subpopulations. A proper distribution leads to ecient evaluation and good solutions. The new technique uses the traditional histogram construction technique for subpopulation formation. Experiments show that the proposed algorithm, GAHisto
(Genetic Algorithm with Histogram technique) when tested with unimodal and multi-modal test function has given good results. This paper is submitted for the special issue
on Novel Intelligent Approach to Multimedia Signal Processing.

Lots of studies worldwide have been carried out to check out the prevalence of Hepatitis C Virus (HCV) in human populations. Spatial data analysis and clustering detection is a vital process in HCV monitoring to discover the area of high risk and to help involved decision makers to draw hypotheses about the cause of disease. Egypt is declared as one of the countries having the highest prevalence rate of HCV worldwide. The anomaly of the HCV infection's distribution in Egypt allowed several researches to identify the reasons that contributed to such widespread of HCV in this country. One way that can help in identification of areas with highest diseases is to give a detailed knowledge about the geographical distribution of HCV in Egypt. To achieve that goal, Data mining analytical tools integrated with GIS can help to visualize the distribution. Thus, the main propose of this paper is to present a spatial distribution of HCV in Egypt using case data obtained from the Egyptian health institute National Hepatology Tropical Medicine Research Institute (NHTMR). The visualization of the spatial analysis distribution by means of GIS allows us to investigate statistical results that are easily interpreted by non-experts.

This paper demonstrates the ability of Genetic Algorithms (GAs) in the identification of dynamical nonlinear systems. The dynamics of the nonlinear systems have been described by first, second and third order terms. GAs were used suc-cessfully to identify the coefficient of these terms. A com-parison between least-square estimation (LSE) and genetic algorithms estimation (GAE) procedures is provided. The comparison was employed based on two factors, number of observations and estimation accuracy. Genetic algorithms show better performance in both noise free and noisy cases.

During the last decade, the technologies related to electric vehicles (EVs) have captured both scientific and industrial interest. Specifically, the subject of the smart charging of EVs has gained significant attention, as it facilitates the managed charging of EVs to reduce disturbances to the power grid. Despite the presence of an extended literature on the topic, the implementation of a framework that allows flexibility in the definition of the decision-making objectives, along with user-defined criteria is still a challenge. Towards addressing this challenge, a framework for the smart charging of EVs is presented in this paper. The framework consists of a heuristic algorithm that facilitates the charge scheduling within a charging station (CS), and the analytic hierarchy process (AHP) to support the driver of the EV selecting the most appropriate charging station based on their needs of transportation and personal preferences. The communications are facilitated by the Open Platform Communications-Unified Architecture (OPC-UA) standard. For the selection of the scheduling algorithm, the genetic algorithm and particle swarm optimisation have been evaluated, where the latter had better performance. The performance of the charge scheduling is evaluated, in various charging tasks, compared to the exhaustive search for small problems.

A joint evaluation of all animals, uses all additive genetic relationships uses all data on all animal jointly It works as a linear model (correcting different effects for each other), jointly estimates animal effects and fixed effects (herds) but has Selection Index properties (Regression with " heritability ")  1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1

This paper considers a design of signal constellations for trans-modulation (constellation rearrangement: CoRe) in a relay system with the multiple links in which the same signal is decoded-and-forwarded to the destination over N time slots, each assigned to the different relay node. Demonstrating that the minimal accumulated squared Euclidean distance (MASED) is not an ultimate design metric to minimize the bit error rate (BER), we choose the BER for the joint log-likelihood ratio (LLR) combining scheme as a design metric, so as to find bit-to-symbol mapping for the multiple relay links. To handle the prohibitive amount of computational complexity associated with an exhaustive search over enumeration of all possible mappings, which grows exponentially with the number of relay links, genetic algorithm is employed for searching for the sub-optimal bitto-symbol mappings in the multiple relay links. We show that a new type of CoRe design without the gray-mapping constraint is critical ...

A novel feature region selection method for robust digital image watermarking is proposed in this paper. This method aims to select a nonoverlapping feature region set, which has the greatest robustness against various attacks and can preserve image quality as much as possible after watermarked. It first performs a simulated attacking procedure using some predefined attacks to evaluate the robustness of every candidate feature region. According to the evaluation results, it then adopts a track-with-pruning procedure to search a minimal primary feature set which can resist the most predefined attacks. In order to enhance its resistance to undefined attacks under the constraint of preserving image quality, the primary feature set is then extended by adding into some auxiliary feature regions. This work is formulated as a multidimensional knapsack problem and solved by a genetic algorithm based approach. The experimental results for StirMark attacks on some benchmark images support our expectation that the primary feature set can resist all the predefined attacks and its extension can enhance the robustness against undefined attacks. Comparing with some well-known feature-based methods, the proposed method exhibits better performance in robust digital watermarking.

This paper describes the application of an organic programming language Gaea in programming complex and flexible multi-agent systems. We consider a soccer game as an example. Using organic programming, we can program soccer players in an extended version of a subsumption architecture, called a dynamic subsumption architecture, which is more flexible and efficient than the original one

Dividing a data set into a training set and a test set is a fundamental component in the preprocessing phase of data mining (DM). Effectively, the choice of the training set is an important factor in deriving good classification rules. Traditional approach for association rules mining divides the dataset into training set and test set based on statistical methods. In

This work approximates the selective harmonic elimination problem using artificial neural networks (ANNs) to generate the switching angles in an 11-level full-bridge cascade inverter powered by five varying dc input sources. Each of the five full bridges of the cascade inverter was connected to a separate 195-W solar panel. The angles were chosen such that the fundamental was kept constant and the low-order harmonics were minimized or eliminated. A nondeterministic method is used to solve the system for the angles and to obtain the data set for the ANN training. The method also provides a set of acceptable solutions in the space where solutions do not exist by analytical methods. The trained ANN is a suitable tool that brings a small generalization effect on the angles' precision and is able to perform in real time (50-/60-Hz time window).

For many water authorities worldwide, one of the greatest potential areas for energy savings is in pump selection and in the related effective scheduling of daily pump operations. The optimal control and operation of an irrigation pumping station is achieved here by first solving the nonlinear governing model using Lagrange Multipliers (LM) and then through Genetic Algorithm (GA) approach. Computation

– Association rule mining may be a crucial and area of the data mining methodology. It aims at finding some hidden information or relationships among the attributes of the data. Association rule mining may be a stimulating area of data mining analysis that discovers correlations between wholly different item sets throughout event information. To enhance the efficiency and accuracy of the proposed work this is incorporating additional measures in simple Genetic Algorithmic rule for such data processing issues. We have used Genetic algorithmic rule to optimize the generated rules and take fitness operate for the improvement and realize the optimum solutions. To compare these two implementations on the generation and fitness they needed in proposed work.