Support vector machine (SVM) has become one of the most popular machine-learning methods during the last years. The design of an efficient model and the proper adjustment of the SVMs parameters are integral to reducing the testing time and enhancing performance. In this paper, a new bipartite objective function consisted of the sparseness property and generalization performance is proposed. Since the proposed objective function is based on selecting fewer numbers of the support vectors, the model complexity is reduced while the performance accuracy remains at an acceptable level. Due to the model complexity reduction, the testing time is decreased and the ability of SVM in practical applications is increased Moreover, to prove the performance of the proposed objective function, a comparative study was carried out on the proposed objective function and the conventional objective function, which is only based on the generalization performance, using the Binary Genetic Algorithm (BGA) and Real-valued vectors GA (RGA). The effectiveness of the proposed cost function is demonstrated based on the results of the comparative study on four real-world datasets of UCI database.
Bayesian Networks (BNs) are graphical models which represent multivariate joint probability distributions which have been used successfully in several studies in many application areas. BN learning algorithms can be remarkably effective in many problems. The search space for a BN induction, however, has an exponential dimension. Therefore, finding the BN structure that better represents the dependencies among the variables is known to be a NP problem. This work proposes and discusses a hybrid Bayes/Genetic collaboration (VOGAC-MarkovPC) designed to induce Conditional Independence Bayesian Classifiers from data. The main contribution is the use of MarkovPC algorithm in order to reduce the computational complexity of a Genetic Algorithm (GA) designed to explore the Variable Orderings (VOs) in order to optimize the induced classifiers. Experiments performed in a number of datasets revealed that VOGAC-MarkovPC required less than 25% of the time demanded by VOGAC-PC on average. In addition, when concerning the classification accuracy, VOGAC-MarkovPC performed as well as VOGAC-PC did.
Distribution of the goods from a producer to a customer is one of the most important tasks of transportation. This paper focuses on the usage of genetic algorithms (GA) for optimizing problems in transportation, namely vehicle routing problem (VRP). VRP falls in the field of NP-hard problems, which cannot be solved in polynomial time. The problem was solved using genetic algorithm with two types of crossover, both including and leaving-out elitism, setting variable parameters of crossover and mutation probability, as well as prevention of creating invalid individuals. The algorithm was programmed in Matlab, tested on real world problem of spare parts distribution for garages, while the results were compared with another heuristic method (Clarke-Wright method). Genetic algorithm provided a better solution than the heuristic Clarke-Wright method.
In this paper some remarks on predictive modeling of traction power consumption and their use in intelligent control systems are stated. Special emphasis is put on discussing neural networks and genetic algorithms for such models described in the second Chapter. In the third Chapter, significant applications of neural networks and genetic algorithms in area of power consumption and train diagram are stated. A methodology of model development and assessment is presented in Chapter 4. In Chapter 5 there are up to now results of the author's traction power consumption prediction coming out from artificial neural network predictive models developed in Mathematica SW environment. Finally, summary and further work are stated in the last Chapter.
Information retrieval systems depend on Boolean queries. Proposed evolution of Boolean queries should increase the performance of the information retrieval system. Information retrieval systems quality are measured in terms of two different criteria, precision and recall. Evolutionary techniques are widely applied for optimization tasks in different areas including the area of information retrieval systems. In information retrieval applications both criteria have been combined in a single scalar fitness function by means of a weighting scheme 'harmonic mean'. Usage of genetic algorithms in the Information retrieval, especially in optimizing a Boolean query, is presented in this paper. Influence of both criteria, precision and recall, on quality improvement are discussed as well.
Calibration of rainfall-runoff model parameters is an inseparable part of hydrological simulations. To achieve more accurate results of these simulations, it is necessary to implement an efficient calibration method that provides sufficient refinement of the model parameters in a reasonable time frame. In order to perform the calibration repeatedly for large amount of data and provide results of calibrated model simulations for the flood warning process in a short time, the method also has to be automated. In this paper, several local and global optimization methods are tested for their efficiency. The main goal is to identify the most accurate method for the calibration process that provides accurate results in an operational time frame (typically less than 1 hour) to be used in the flood prediction Floreon\textsuperscript{+} system. All calibrations were performed on the measured data during the rainfall events in 2010 in the Moravian-Silesian region (Czech Republic) using our in-house rainfall-runoff model.
As the volume and variety of information sources, especially on the World Wide Web (WWW), continue to grow, the requirements imposed on search applications are steadily increasing. The amount of available data is growing and so do the user demands. Search application should provide the users with accurate, sensible responses to their requests. It is difficult to provide information that accurately matches user information needs. Search effectiveness can be seen as the accuracy of matching user information needs against the retrieved information. There are problems emerging: users often do not present search queries in the form that optimally represents their information need, the measure of a document's relevance is often highly subjective between different users, and information sources might contain heterogeneous documents, in multiple formats and the representation of documents is not unified. This contribution presents a proposal to improve web search effectiveness via evolutionary optimization of the Boolean and vector search queries based on individual user models.
This páper addresses several issues related to the approximate solution of the Single Machine Scheduling problém with sequence-dependent setup times using metaheuristic methods. Instances with known optimal solution are solved using a memetic algorithm and a multiple start approach. A fitness landscape analysis is also conducted on a subset of instances to understand the behavior of the two approaches during the optimization process. We also present a novel way to create instances with known optimal Solutions from the optimally solved asymmetric travelling salesman problém (ATSP) instances. Finally we argue for the test set of instances to be ušed in future works as a convenient performance benchmark.
The demand for mobile communication has been steadily increasing
in recent years. With the limited frequency spectrum, the problem of channel assignment becomes increasingly important, i.e., how do we assign the calls to the available channels so that the interference is minimized while the demand is met? This problém is known to belong to a class of very difficult combinatorial optimization problems. In this paper, we apply the formulation of Ngo and Li with genetic algorithms to ten benchmarking problems. Interference-free Solutions cannot be found for soine of these problems; however, the approach is able to minimize the interference significantly. The results demonstrate the effectiveness of genetic algorithms in searching for optirnal Solutions in this complex optimization problem.
The structure of procedures with genetic algorithms using methodological tools of constructive systém theory is described. The model of genetic code of systém is used for the transrnissioii of species characteristics with the emphasis on non-living and formal objects.