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Performance analysis of the partial use of a local optimization operator on the genetic algorithm for the Travelling Salesman Problem

Performance analysis of the partial use of a local optimization operator on the genetic algorithm for the Travelling Salesman Problem

Background: The Travelling Salesman Problem is an NP-hard problem in combinatorial optimization with a number of practical implications. There are many heuristic algorithms and exact methods for solving the problem. Objectives: In this paper we study the influence of hybridization of a genetic algorithm with a local optimizer on solving instances of the Travelling Salesman Problem. Methods/Approach: Our algorithm uses hybridization that occurs at various percentages of generations of a genetic algorithm. Moreover, we have also studied at which generations to apply the hybridization and hence applied it at random generations, at the initial generations, and at the last ones. Results: We tested our algorithm on instances with sizes ranging from 76 to 439 cities. On the one hand, the less frequent application of hybridization decreased the average running time of the algorithm from 14.62 sec to 2.78 sec at 100% and 10% hybridization respectively, while on the other hand, the quality of the solution on average deteriorated only from 0.21% till 1.40% worse than the optimal solution. Conclusions: In the paper we have shown that even a small hybridization substantially improves the quality of the result. Moreover, the hybridization in fact does not deteriorate the running time too much. Finally, our experiments show that the best results are obtained when hybridization occurs in the last generations of the genetic algorithm.

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Quality Improvement of a Gear Transmission by Means of Genetic Algorithm

Abstract

The article deals with the issue of quality improvement of a gear transmission by optimizing its geometry with the use of genetic algorithms. The optimization method is focused on increasing productivity and efficiency of the pump and reducing its pulsation. The best results are tested on mathematical model and automatically modelled in 3D be means of PTC Creo Software. The developed solution proved to be an effective tool in the search for better results, which greatly improved parameters of pump especially reduced flow pulsation.

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Using Genetic Algorithm to Optimize Weights in Data Mining Task

Using Genetic Algorithm to Optimize Weights in Data Mining Task

This paper considers an application of genetic algorithm (GA) to optimize weights in data mining task. Data mining tasks usually have datasets containing a large number of records and features that will be processed using, for example, created classification rules. As a result, by using classical method to classify a large number of records and features, a high classification error value will be obtained. To solve this problem, the genetic algorithm was applied to find for each feature the weight that would reduce classification error value.

As a classical method, the k-nearest neighbour (KNN) classifier was chosen and the modified genetic algorithm was applied to optimize the weight. Based on the joint application of genetic and k-nearest neighbour algorithms, the GA/KNN hybrid algorithm was developed. As a result, the developed hybrid algorithm provides a stable classification error reducing regardless of the number of records and features, and also of the chosen number of neighbours. In the GA block the modified crossover and mutation works in each generation with identical intensity and cannot provide debasing of the individual.

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Hyperinsulinemic Hypoglycemias in Infancy and Childhood - Diagnostic Therapeutic Algorithm with Contribution of Two Cases

Hyperinsulinemic Hypoglycemias in Infancy and Childhood - Diagnostic Therapeutic Algorithm with Contribution of Two Cases

Hypoglycemia is not an independent diagnosis. It is a pathophysiological syndrome whose cause needs to be identified. Identifying it is just the first step to making the diagnosis as precisely as possible and to preventing brain damage. Timely diagnosis and treatment are factors of paramount importance for the prognosis of affected patients.

The aim of this study was to present two of our patients with hyperinsulinemic hypoglycemia because of the rarity of the condition and to propose a diagnostic-therapeutic algorithm of hypoglycemic syndrome in childhood.

Identifying the genetic mutations using DNA analysis for both children enabled us to determine the prognosis and to provide genetic counseling about the next pregnancies in the affected families.

We make a detailed classification of different types of hypoglycemia and the various therapeutic modalities: dietary, medicinal and surgical depending on the etiology.

It is concluded that the highly specialized examinations which ensure the etiological diagnose, treatment, prognosis and genetic consultation demand the participation of a well trained medical team - both in the clinical division and in the laboratory.

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Optimization of Satellite Combination in Kinematic Positioning Mode with the Aid of Genetic Algorithm

Optimization of Satellite Combination in Kinematic Positioning Mode with the Aid of Genetic Algorithm

The basis of high precision relative positioning is the use of carrier phase measurements. Data differencing techniques are one of the keys to achieving high precision positioning results as they can significantly reduce a variety of errors or biases in the observations and models. Since GPS observations are usually contaminated by many errors such as the atmospheric biases, the receiver clock bias, the satellite clock bias, and so on, it is impossible to model all systematic errors in the functional model. Although the data differencing techniques are widely used for constructing the functional model, some un-modeled systematic biases still remain in the GPS observations following such differencing. Another key to achieving high precision positioning results is to fix the initial carrier phase ambiguities to their theoretical integer values. To obtain a high percentage of successful ambiguity-fixed rates, noisy GPS satellites have to be identified and removed from the data processing step. This paper introduces a new method using genetic algorithm (GA) to optimize the best combination of GPS satellites which yields the highest number of successful ambiguity-fixed solutions in kinematic positioning mode. The results indicate that the use of GA can produce higher number of ambiguity-fixed solutions than the standard data processing technique.

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Genetic Algorithm Based Clustering for Large-Scale Sensor Networks

Abstract

Despite the success of various clustering algorithms for Wireless Sensor Networks (WSNs), there are few works that consider the interference between clusters. Obviously, interference-free clustering makes the communication more efficient and achieves energy saving. In this paper we propose a new clustering method for large-scale sensor networks. With this method the network is partitioned into clusters. Intra-cluster communication in a cluster has no interference by its neighbor clusters. Moreover, the proposed clustering is based on a Genetic Algorithm (GA), which can achieve optimal performance in terms of the number of isolated nodes. This is demonstrated by the simulation analysis.

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Optimization of Welding Process using a Genetic Algorithm

Abstract

Welding is a complex technological process in which local heating takes place up to the melting point of the connecting and the additional material. Phase and crystallization processes are a strong non-linear function of the cooling rate. This non-linear function multiplies the complexity of the numerical simulation and optimization of the welding process. Lately, optimization with genetic algorithm has become the trend to optimize systems that behave in a non-linear manner and contain a number of local extremes. Genetic algorithm is therefore a method by which we seek an absolute extreme. It is a method which seeks a solution to near absolute extreme. In this paper the use of the genetic algorithm for welding process optimization is described.

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Facial Composite System Using Genetic Algorithm

Abstract

The article deals with genetic algorithms and their application in face identification. The purpose of the research is to develop a free and open-source facial composite system using evolutionary algorithms, primarily processes of selection and breeding. The initial testing proved higher quality of the final composites and massive reduction in the composites processing time. System requirements were specified and future research orientation was proposed in order to improve the results.

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The Optimization of COCOMO Model Coefficients Using Genetic Algorithms

Abstract

Nowadays there are many models for software development cost estimation, providing project managers with helpful information to make the right decisions. One of such well-known mathematical models is the COCOMO model. To estimate costs and time, this model uses coefficients, which were determined in 1981 by means of the regression analysis of statistical data based on 63 different types of project data. Using these coefficients for a modern project, the appraisal may not be accurate; therefore, the aim of this paper is to optimize the model coefficients with genetic algorithms. Genetic algorithms are evolutionary methods for optimization. To evaluate population, the genetic algorithm will use a set of descriptive attributes of several software development projects. These attributes are the number of lines of a code, costs and implementation time of a project. Project costs estimated by means of the COCOMO model will be compared with the real ones, this way evaluating the fitness of an individual in the population of possible solutions

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Investigation of the effect of thermal stress on the interface damage of hybrid biocomposite materials

Abstract

In this paper, we have studied the effect of thermal stress on the damage of fiber-matrix interface of a hybrid biocomposite composed of two natural fibers, Hemp, Sisal, and Starch matrix. Our genetic modeling used the nonlinear acoustic technique based on Cox’s analytical model, Weibull’s probabilistic model, and Lebrun’s model describing the thermal stress by the two coefficients of expansion. The stress applied to our representative elementary volume is a uni-axial tensile stress.

The numerical simulation shows that the Hemp- Sisal/Starch hybrid biocomposite is most resistant to thermal stresses as compared with Hemp/Starch biocomposite. It also shows that hybrid biocomposite materials have a high resistance to applied stresses (mechanical and thermal) compared to traditional materials and biocomposite materials. The results obtained in our study coincide perfectly with the results of Antoine et al., which showed through experimental tests that natural fibers perfectly improve the mechanical properties of biocomposite materials.

Open access