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Grid computing is a computational paradigm that emerged to ‎‎handle the increasing demand for ‎computational resources. Several metaheuristics methods ‎‎have been applied ‎to tackle the grid task scheduling problem. ‎‎These metaheuristics generally generate good but not optimal ‎‎task ‎schedules. The aim of this paper is to design and ‎‎implement a grid task scheduling mechanism to map clients' tasks to ‎‎ ‎available resources in order to finish the submitted tasks ‎‎within the optimal execution time. The paper proposes ‎an ‎‎enhanced time shared metaheuristics mechanism based on ‎‎Firefly Algorithm to ‎‎improve the grid job scheduling process. The proposed mechanism utilizes the Smallest Position ‎Value (SPV) technique to handle the scheduling problem as ‎permutations. Experiments using ‎‎simulations and real workload traces were ‎conducted to study ‎‎the performance of the proposed enhanced time shared ‎‎metaheuristic scheduling mechanism. ‎Empirical results revealed ‎‎that the proposed timed shared ‎metaheuristic algorithm can efficiently reduce the makespan time to 1851 compared with 3482, 3185 for Tabu search and genetic algorithm, respectively. [ABSTRACT FROM AUTHOR]

Inspired by classical feature descriptors in motion matching, this paper proposes a multimodal failure matching point collection method, which is defined as FMP. FMP is, in fact, a collection of unstable features with a low matching degree in the conventional matching task. Based on FMP, a novel model for the saliency detection of motion object is developed. Models are evaluated on the DAVIS and SegTrackv2 datasets and compared with recently advanced object detection algorithms. The comparison results demonstrate the availability and effectiveness of FMP in the detection of motion object saliency. [ABSTRACT FROM AUTHOR]

A stroke is a medical disorder in which blood arteries in the brain rupture, causing brain damage. Symptoms may appear when the brain's blood supply and other nutrients are cut off. According to the World Health Organization, Stroke is the leading cause of death and disability globally. Early recognition of the multiple warning signs of a stroke helps reduce the severity of the stroke. The paper presents a modified version of the Genetic Folding algorithm to predict stroke based on symptoms. Considerable Machine Learning models, including Logistic Regression, Decision Tree, Random Forest, Naïve Bayes, Support Vector Machine, and the proposed Minimal Genetic Folding, were compared to forecast the probability of having a stroke in the brain using a variety of physiological characteristics. The proposed minimal Genetic Folding approach has been developed using the open-access Stroke Prediction dataset using minimal kernel operators. The datasets generated and/or analyzed during the current study are available in the Kaggle repository. With an accuracy of 83.2%, the proposed minimal Genetic Folding approach outperformed Logistic Regression by 4.2%, Naïve Bayes by 1.2%, Decision Tree by 17.2%, and Support Vector Machine by 83.2%. The area under the curve of the proposed model is much more significant than earlier research by 7%, demonstrating that this model is more dependable and was the top-performing algorithm. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the Levenberg-Marquardt (LM) algorithm can not train online radial basis function (RBF) neural network and the deficiency in the RBF network structure design methods, this paper proposes an online self-adaptive algorithm for constructing RBF neural network (OSA-RBFNN) based on LM algorithm. Thus, the ideas of the sliding window method and online structure optimization methods are adopted to solve the proposed problems. On the one hand, the sliding window method enables the RBF network to be trained online by the LM algorithm making the RBF network more robust to the changes in the learning parameters and faster convergence compared with the other investigated algorithms. On the other hand, online structure optimization can adjust the structure of the RBF network based on the information of training errors and hidden nodes to track the non-linear time-varying systems, which helps to maintain a compact network and satisfactory generalization ability. Finally, verified by simulation analysis, it is demonstrated that OSA-RBFNN exhibits a compact RBF network. [ABSTRACT FROM AUTHOR]

Artificial Neural Networks (ANNs) models play an increasingly significant role in accurate time series prediction tools. However, an accurate time series forecasting using ANN requires an optimal model. Hence, great forecasting methods have been developed from optimized ANN models. Most of them focus more on input variables selection and preprocessing, topologies selection, optimum configuration and its associated parameters regardless of their input variables disposition. This paper provides an investigation of the effects of input variables disposition on ANNs models on training and forecasting performances. After investigation, a new ANNs optimization approach is proposed, consisting of finding optimal input variables disposition from the possible combinations. Therefore, a modified Back-Propagation neural networks training algorithm is presented in this paper. This proposed approach is applied to optimize the feed-forward and recurrent neural networks architectures; both built using traditional techniques, and pursuing to forecast the wind speed. Furthermore, the proposed approach is tested in a collaborative optimization method with single-objective optimization technique. Thus, Genetic Algorithm Back-Propagation neural networks aim to improve the forecasting accuracy relative to traditional methods was proposed. The experiment results demonstrate the requirement to take into consideration the input variables disposition to build a more optimal ANN model. They reveal that each proposed model is superior to its old considered model in terms of forecasting accuracy and thus show that the proposed optimization approach can be useful for time series forecasting accuracy improvement. [ABSTRACT FROM AUTHOR]

Direct marketing identifies customers who buy, more probable, a specific product to reduce the cost and increase the response rate of a marketing campaign. The advancement of technology in the current era makes the data collection process easy. Hence, a large number of customer data can be stored in companies where they can be employed to solve the direct marketing problem. In this paper, a novel Bayesian method titled correlation-augment naïve Bayes (CAN) is proposed to improve the conventional naïve Bayes (NB) classifier. The performance of the proposed method in terms of the response rate is evaluated and compared to several well-known Bayesian networks and other well-known classifiers based on seven real-world datasets from different areas with different characteristics. The experimental results show that the proposed CAN method has a much better performance compared to the other investigated methods for direct marketing in almost all cases. [ABSTRACT FROM AUTHOR]

Great deluge (GD) algorithm same as other metaheuristics can solve feature selection problem. The GD imitates that in a great deluge someone climbing a hill and attempt to progress in any direction that does not get his/her feet wet in the expectation of discovering a way up when the water Level rises. The drawbacks of GD are: 1) a local search, which may lead the algorithm toward a local optima and 2) a challenging estimation of quality of the final solution in solving most of the problems. In this paper, for the first issue, a population-based great deluge (popGD) algorithm with additional recurrence operation is proposed. This operation is an imitation of no progress of hill climber after a long time; the climber tries to move small steps even downward in hope of finding better way to climb. For the second problem, a technique with an automate alteration of the Level is proposed. The statistical analysis of the results from 25 test functions and 18 benchmark feature selection problems supports the ability of the method. Finally a real-world academician data are employed to perform feature selection and execute classification result with selected features. [ABSTRACT FROM AUTHOR]

This paper presents a new hybrid PPSOGSA algorithm based on the phasor particle swarm optimization (PPSO) and gravitational search algorithm (GSA) for solving the Volt/VAr/THDv control optimization problem in radial distribution systems with non-linear loads. The PPSOGSA's performance is analyzed and evaluated using the standard IEEE 30-bus radial distribution test system. The optimal solutions are obtained based on the following three optimization criteria: minimization of total active power losses, minimization of the voltage deviation, and a simultaneous minimization of total active power losses and voltage deviation. The voltage total harmonic distortion levels (THDv) are estimated using the decoupled harmonic power flow (DHPF) algorithm. Simulation results, obtained using the proposed PPSOGSA-based approach, are compared with those obtained using the other optimization algorithms such as PSO, PPSO, GSA, and PSOGSA. It is shown that the PPSOGSA-based approach has better performance in comparison to the other optimization techniques. [ABSTRACT FROM AUTHOR]

Sine-cosine algorithm (SCA) has found a widespread application in various engineering optimization problems. However, SCA suffers from premature convergence and insufficient exploitation. Cylindricity error evaluation is a typical engineering optimization problem related to the quality of cylindrical parts. A hybrid greedy sine-cosine algorithm with differential evolution (HGSCADE) is developed in this paper to solve optimization problems and evaluate cylindricity error. HGSCADE integrates the SCA with the opposition-based population initialization, the greedy search, the differential evolution (DE), the success history-based parameter adaptation, and the Levy flight-based local search. HGSCADE is tested on the CEC2014 benchmark functions and is employed in cylindricity error evaluation. The results show the superiority of HGSCADE to other state-of-the-art algorithms for the benchmark functions and cylindricity error evaluation. [ABSTRACT FROM AUTHOR]

Palm oil is a major contributor to Malaysia's GDP in the agriculture sector. The sheer vastness of oil palm plantations requires a huge effort to administer. An oil palm plantation in regards to the irrigation process, fertilization, and planning for planting new trees require an audit process to correctly count the oil palm trees. Currently, the audit is done manually using aerial view images. Therefore, an effective and efficient method is imperative. This paper proposes a new automatic end-to-end method based on deep learning (DL) for detection and counting oil palm trees from images obtained from unmanned aerial vehicle (UAV) drone. The acquired images were first cropped and sampled into small size of sub-images, which were divided into a training set, a validation set, and a testing set. A DL algorithm based on Faster-RCNN was employed to build the model, extracts features from the images and identifies the oil palm trees, and gives information on the respective locations. The model was then trained and used to detect individual oil palm tree based on data from the testing set. The overall accuracy of oil palm tree detection was measured from three different sites with 97.06%, 96.58%, and 97.79% correct oil palm detection. The results show that the proposed method is more effective, accurate detection, and correctly counts the number of oil palm trees from the UAV images. [ABSTRACT FROM AUTHOR]

Direct visual odometry (DVO) is an important vision task which aims to obtain the camera motion via minimizing the photometric error across the different correlated images. However, the previous research on DVO rarely considered the motion bias and only calculated using single direction, therefore potentially ignoring useful information compared with leveraging diverse directions. We assume that jointly considering forward and backward calculation can improve the accuracy of pose estimation. To verify our assumption and solid this contribution, in this paper, we test various combination of direct dense methods, including different error metrics, e.g., (intensity, gradient magnitude), alignment strategies (Forward-Compositional, Inverse-Compositional), and calculation directions (forward, backward, and bi-direction). We further study the issue of motion bias in RGB-D visual odometry and propose four strategy options to improve pose estimation accuracy, e.g., joint bi-direction estimation; two stage bi-direction estimation; transform average with weights; and transform fusion with covariance. We demonstrate the effectiveness and efficiency of our proposed algorithms across a range of popular datasets, e.g., TUM RGB-D and ICL-NUIM, in which we achieve an impressive performance through comparing with state of the art methods and provide benefits for existing RGB-D visual odometry and visual SLAM systems. [ABSTRACT FROM AUTHOR]

AI technology has developed so fast, and it has been applied to the commercial area. In order to predict the customer preference and adjust the placement of product or advertisement, etc., the intelligent surveillance video analysis technique has been proposed to gather the sufficient customer information and realize crowd counting and density map drawing. In this paper, a series of deep learning techniques are adopted to realize surveillance video analysis. This work covers different subproblems such as object detection, tracking and human identification. A skeleton recognition algorithm is adopted instead of object detection algorithm to overcome the severe occlusion problem. A multiple human tracking algorithm combing the human re-identification technology is adopted to realize the human tracking and counting. Finally, the density map and statistics information are obtained which can be used to evaluate and adjust the current business plan. A real store surveillance video is analyzed by the algorithm, and the results show the advantage of the algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a novel algorithm so-called VFC4.5 for building decision trees. It proposes an adaptation of the way C4.5 finds the threshold of a continuous attribute. Instead of finding the threshold that maximizes gain ratio, the paper proposes to simply reduce the number of candidate cut points by using arithmetic mean and median to improve a reported weakness of the C4.5 algorithm when it deals with continuous attributes. This paper will focus primarily on the theoretical aspects of the VFC4.5 algorithm. An empirical trials, using 49 datasets, show that, in most times, the VFC4.5 algorithm leads to smaller decision trees with better accuracy compared to the C4.5 algorithm. VFC4.5 gives excellent accuracy results as C4.5 and it is much faster than the VFDT algorithm. [ABSTRACT FROM AUTHOR]

Context-aware collaborative filtering (CACF) is an effective approach for adapting recommendations under users' specific contextual situations and aims to improve predictive accuracy for Context-aware recommender systems (CARSs). Incorporating context in recommender systems (RSs) considering the equal importance to all contextual dimensions is not appropriate for seeking an intelligent and useful recommendation. In this paper, we propose a Real-coded Genetic Algorithm (RCGA) based CARS framework that exploits contextual pre-filtering and contextual modeling paradigms into CACF with appropriate context feature weights for enhancing accuracy as well as the diversity of the recommendation list. Further to alleviate the data sparsity, an effective missing value prediction (EMVP) algorithm is applied into proposed framework. The accuracy based on RCGA is compared with other two schemes: Support Vector Machine (SVM) and Particle Swarm Optimization (PSO), and RCGA has shown better results. Experimental results based on real-world datasets have clearly established the effectiveness of our proposed CARS schemes. [ABSTRACT FROM AUTHOR]

The Traveling Salesman Problem (TSP) is one of the most popular Combinatorial Optimization Problem. It is well solicited for the large variety of applications that it can solve, but also for its difficulty to find optimal solutions. One of the variants of the TSP is the Generalized TSP (GTSP), where the TSP is considered as a special case which makes the GTSP harder to solve. We propose in this paper a new memetic algorithm based on the well-known Breakout Local Search (BLS) metaheuristic to provide good solutions for GTSP instances. Our approach is competitive compared to other recent memetic algorithms proposed for the GTSP and gives at the same time some improvements to BLS to reduce its runtime. [ABSTRACT FROM AUTHOR]

The performance of various population-based advanced optimization algorithms has been significantly improved by using the multi-population search scheme. The multi-population search process improves the diversity of solutions by dividing the total population into a number of sub-populations groups to search for the best solution in different areas of a search space. This paper proposes improved optimization algorithms based on self-adaptive multi-population for solving engineering design optimization problems. These proposed algorithms are based on Rao algorithms which are recently proposed simple and algorithm-specific parameter-less advanced optimization algorithms. In this work, Rao algorithms are upgraded with the multi-population search process to enhance the diversity of search. The number of sub-populations is changed adaptively considering the strength of solutions to control the exploration and exploitation of the search process. The performance of proposed algorithms is investigated on 25 unconstrained benchmark functions and 14 complex constrained engineering design optimization problems. The results obtained using proposed algorithms are compared with the various advanced optimization algorithms. The comparison of results shows the effectiveness of proposed algorithms for solving engineering design optimization problems. The significance of the proposed methods has proved using a well-known statistical test known as "Friedman test." Furthermore, the convergence plots are illustrated to show the convergence speed of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Neural tensor network (NTN) has been recently introduced to complete Resource Description Framework (RDF) knowledge bases, which has been the state-of-the-art in the field so far. An RDF knowledge base includes some facts from the real world shown as RDF "triples." In the previous methods, an objective function has been used for training this type of network, and the network parameters should have been set in a way to minimize the function. For this purpose, a classic nonlinear optimization method has been used. Since many replications are needed in this method to get the minimum amount of the function, in this paper, we suggest to combine meta-heuristic optimization methods to minimize the replications and increase the speed of training consequently. So, this problem will be improved using some meta-heuristic algorithms in this new approach to specify which algorithm will get the best results on NTN and its results will be compared with the results of the former methods finally. [ABSTRACT FROM AUTHOR]

There are a number of algorithms for the solution of continuous optimization problems. However, many practical design optimization problems use integer design variables instead of continuous. These types of problems cannot be handled by using continuous design variables-based algorithms. In this paper, we present a multi-objective integer melody search optimization algorithm (MO-IMS) for solving multi-objective integer optimization problems, which take design variables as integers. The proposed algorithm is a modified version of single-objective melody search (MS) algorithm, which is an innovative optimization algorithm, inspired by basic concepts applied in harmony search (HS) algorithm. Results show that MO-IMS has better performance in solving multi-objective integer problems than the existing multi-objective integer harmony search algorithm (MO-IHS). Performance of proposed algorithm is evaluated by using various performance metrics on test functions. The simulation results show that the proposed MO-IMS can be a better technique for solving multi-objective problems having integer decision variables. [ABSTRACT FROM AUTHOR]

Pathological electrocardiogram is often used to diagnose abnormal cardiac disorders where accurate classification of the cardiac beat types is crucial for timely diagnosis of dangerous conditions. However, accurate, timely, and precise detection of arrhythmia-types like premature ventricular contraction is very challenging as these signals are multiform, i.e. a reliable detection of these requires expert annotations. In this paper, a multivariate statistical classifier that is able to detect premature ventricular contraction beats is presented. This novel classifier can be trained with a very sparse amount of expert annotated data. To enable this, the dimensionality of the feature vector is kept very low, it uses strong designed features and a regularization mechanism. This approach is compared to other classifiers by using the MIT-BIH arrhythmia database. It has been found that the average accuracy, specificity, and sensitivity are above 96%, which is superior given the sparse amount of training data. [ABSTRACT FROM AUTHOR]

A good method of combining Bayesian networks (BNs) should be a generic one that ensures a combined BN meets three important criteria of avoiding cycles, preserving conditional independencies, and preserving the characteristics of individual BN parameters. All combination methods assumed that there is an ancestral ordering shared by individual BNs. If this assumption is violated, then avoiding cycles may be inefficient. In this paper, without considering an ancestral ordering, we introduce a novel method for aggregation of BNs. For this purpose, we first combine the BNs using the modification of the method introduced by Feng et al. We then use the simulated annealing algorithm for getting an acyclic graph in which the minimum arcs have been removed. Using this method, most of the conditional independencies are preserved. We compare the results of the proposed method with the two classical BNs combination methods; union and intersection, and hence to demonstrate the distinctive advantages of the proposed BNs combination method. [ABSTRACT FROM AUTHOR]

To optimize anthropogenic material streams, the production process, as well as the quality of the products, must be known. With knowledge of these requirements, it is possible to use extra applied algorithms—in this case evolutionary algorithms as part of artificial intelligence—for the optimization of these secondary material streams. The benefit of this application is the fast and precise calculation of the local and global optima of the optimizing problem. This calculation method uses the benefits of the biological reproduction by applications of mutation, selection, and recombination to find one of the best results in a huge amount of possible and potential results. For the use of secondary materials in the paper production it could be proven that in spite of high quotes of secondary materials in different paper classes, there are some paper classes in which the amount of secondary material could be raised without losing any quality. [ABSTRACT FROM AUTHOR]

This paper presents a subgroup discovery algorithm APRIORI-SD, developed by adapting association rule learning to subgroup discovery. The paper contributes to subgroup discovery, to a better understanding of the weighted covering algorithm, and the properties of the weighted relative accuracy heuristic by analyzing their performance in the ROC space. An experimental comparison with rule learners CN2, RIPPER, and APRIORI-C on UCI data sets demonstrates that APRIORI-SD produces substantially smaller rulesets, where individual rules have higher coverage and significance. APRIORI-SD is also compared to subgroup discovery algorithms CN2-SD and SubgroupMiner. The comparisons performed on U.K. traffic accident data show that APRIORI-SD is a competitive subgroup discovery algorithm. [ABSTRACT FROM AUTHOR]

Monoblock centrifugal pumps play an important role in a variety of engineering applications such as in the food industry, in wastewater treatment plants, in agriculture, in the oil and gas industry, in the paper and pulp industry, and others. Condition monitoring of the various mechanical components of centrifugal pumps becomes essential for increasing productivity and reducing the number of breakdowns. Vibration-based continuous monitoring and analysis using machine learning approaches are gaining momentum. Particularly, artificial neural networks and fuzzy logic have been employed for continuous monitoring and fault diagnosis. This article presents the use of the J48 algorithm for fault diagnosis through discrete wavelet features extracted from vibration signals of good and faulty conditions of the components of a centrifugal pump. The classification accuracies of different discrete wavelet families were calculated and compared in order to find the best wavelet for the fault diagnosis of the centrifugal pump. [ABSTRACT FROM AUTHOR]

In this paper we tackle the sailing strategies problem, a stochastic shortest-path Markov decision process. The problem of solving large Markov decision processes accurately and quickly is challenging. Because the computational effort incurred is considerable, current research focuses on finding superior acceleration techniques. For instance, the convergence properties of current solution methods depend, to a great extent, on the order of backup operations. On one hand, algorithms such as topological sorting are able to find good orderings, but their overhead is usually high. On the other hand, shortest path methods, such as Dijkstra's algorithm, which is based on priority queues, have been applied successfully to the solution of deterministic shortest-path Markov decision processes. Here, we propose improved value iteration algorithms based on Dijkstra's algorithm for solving shortest path Markov decision processes. The experimental results on a stochastic shortest-path problem show the feasibility of our approach. [ABSTRACT FROM PUBLISHER]

This paper describes a novel algorithm for finding the most important relations with the use of data mining. As an example application, the impact of high-level knowledge on economic welfare was analyzed. Our approach, based on interactive data mining, not only helps to discover the most relevant models, but also enables an evaluation of their relevance. The approach is specialized for the analysis of macroeconomic data that often contains incomplete and noisy attributes and, initially, complex relations, where several relations are statistically seemingly important, but only a few are indeed the most relevant. Although data mining algorithms are designed to detect the relevant attributes, irrelevant attributes often appear in the models due to chance-choice, reducing their quality and understandability, and consequently even leading to incorrect conclusions. We present our method and show its application at finding the most relevant relations between high-level knowledge and the state of development of a country. [ABSTRACT FROM AUTHOR]

This paper proposes a neural-based predictive control algorithm for online control of a force-acting industrial hydraulic actuator. In the algorithm, a multilayer feedforward neural network is employed to modeling the highly nonlinear hydraulic actuator. The nonlinear neural model is instantaneously linearized at each sampling point. Estimated parameters from the linearized model are used in the generalized predictive control (GPC) algorithm to control the contact force. Simulation and experimental results show that the neural-based predictive controller can adapt to different environments and keep the contact force in a desired value despite high nonlinearity and uncertainty in the hydraulic actuator system. [ABSTRACT FROM AUTHOR]

In a statistical machine translation system (SMTS), decoding is the process of finding the most likely translation based on a statistical model, according to previously learned parameters. The success of an SMTS is strongly dependent on the quality of its decoder. Most of the SMTS's published in current literature use approaches based on traditional optimization methods and heuristics. On the other hand, over the last few years there has been a rapid increase in the use of metaheuristics. These kinds of techniques have shown to be able to solve difficult search problems in an efficient way for a wide number of applications. This paper proposes a new approach based on evolutionary hybrid algorithms to translate sentences in a specific technical context. The algorithm has been enhanced by adaptive parameter control. The tests are carried out in the context of Spanish and then translated to English. The experimental results validate the superior performance of our method in contrast to a statistical greedy decoder. We also compare our new approach to the existing public domain general translators. [ABSTRACT FROM AUTHOR]

In recent years, the KDD process has been advocated to be an iterative and interactive process. It is seldom the case that a user is able to answer immediately all his questions on date with a single query. On the contrary, the work-flow of the typical user consists of several steps in which he/she iteratively refines the extracted knowledge by inspecting previous results and posing new queries. Given this view of the KDD process, in order to reduce the computational effort, it becomes crucial to have KDD systems that are able to exploit past results. This is especially true in environments in which the system knowledge base is the result of many discoveries on data made separately by the collaborative effort of different users. In this paper, we consider the problem of mining frequent association rules from database relations. We first model a general, constraint-based, mining language for this task. Then, we propose an algorithm that answers such queries reusing past results. In particular, this solution is effective for a new class of constraints, called context dependent, which are more difficult than the traditionally studied item dependent constraints. Nevertheless, we show that some typical queries of important application domains, such as market stock trading, analysis of web log, and gene microarrays in bioinformatics, have context-dependent constraints. We show with a set of experiments in these application domains that the proposed solution with an incremental approach is both effective and viable. [ABSTRACT FROM AUTHOR]

This paper proposes an enhanced Ant Colony Optimization (ACO) metaheuristic called ACO-TS to attack the minimum dominating set (MDS) problem. One of the recognized difficulties faced by ACO in its original form is premature convergence, which produces less satisfactory solutions. We propose a way to encourage a higher degree of exploration of the search space by incorporating a technique based on a concept borrowed from genetic algorithms called tournament selection. Instead of always following the standard mechanism for selecting the next solution component, an ant would make its decision based on the outcome of a tournament between randomly selected allowable components. The frequency of the tournament selection is controlled by a probability measure. The use of tournament selection is coupled with an iteration-best pheromone update. To evaluate the enhanced ACO, we consider the MDS problem formulated from ad hoc network clustering. A comparison with its original form shows that the enhanced ACO produces better solutions using fewer number of cycles. We also empirically demonstrate that the proposed ACO produces better solutions than a genetic algorithm. Finally, we argue, based on empirical results, why the tournament selection approach is preferable to a pure random selection method. [ABSTRACT FROM AUTHOR]

Students that enroll in the undergraduate program on informatics at the Hellenic Open University (HOU) demonstrate significant difficulty in advancing beyond the introductory course. We have embarked in an effort to analyze their academic performance throughout the academic year, as measured by homework assignments, and attempt to derive short rules that explain and predict success or failure in the final exams. In this paper we review previous approaches, compare them with genetic algorithm-based induction of decision trees, and argue why our approach has a potential for developing into an alert tool. [ABSTRACT FROM AUTHOR]

This paper shows how machine learning can help in analyzing and understanding historical change. Using data from the Canadian census of 1901, we discover the influences on bilingualism in Canada at the beginning of the last century. The discovered theories partly agree with, and partly complement, the existing views of historians on this question. Our approach, based around a decision tree, not only infers theories directly from data, but also evaluates existing theories and revises them to improve their consistency with the data. One novel aspect of this work is the use of confidence intervals to determine which factors are both statistically and practically significant, and thus contribute appreciably to the overall accuracy of the theory. When inducing a decision tree directly from data, confidence interrvals determine when new tests should be added. If an existing theory is being evaluated, confidence intervals also determine when old tests should be replaced, or deleted, to improve the theory. Our aim is to minimize the changes made to an existing theory to accommodate the new data. To this end, we propose a semantic measure of similarity between trees and demonstrate how this can be used to limit the changes made. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new method based on evolutionary computation for setting the metaparameters of reinforcement learning in order to match the demands of the task and reduce the learning time. In our method, we encode the metaparameters as the agent's genes and take the metaparameters of best-performing agents in the next generation. We investigate the influence of metaparameters on the agent learned policy and learning time. The results show that appropriate settings of metaparameters found by evolution have a great effect on the learning time and are strongly dependent on each other. In addition, by using the Cyber Rodent robot, we verified that metaparameters evolved in simulation are helpful for learning in real hardware. [ABSTRACT FROM AUTHOR]

Priori-like algorithms for association rules mining have relied on two user-specified thresholds: minimum support and minimum confidence. There are two significant challenges to applying these algorithms to real-world applications: database-dependent minimum-support and exponential search space. Database-dependent minimum-support means that users must specify suitable thresholds for their mining tasks though they may have no knowledge concerning their databases. To circumvent these problems, in this paper, we design an evolutionary mining strategy, namely the ARMGA model, based on a genetic algorithm. Like general genetic algorithms, our ARMGA model is effective for global searching, especially when the search space is so large that it is hardly possible to use deterministic searching method. [ABSTRACT FROM AUTHOR]

As the quietness of vehicles has improved in recent years, there have been stricter requirements to reduce gear vibration and noise and thereby improve transmission quality. The prediction of gear vibration and noise has always been a major concern in gear design. It is widely accepted that the acoustic noise generated by a pair of gears is strongly related to the gears transmission error. Recently, greater emphasis has been placed to further optimize the gear tooth parameters in order to reduce transmission error and, subsequently, improve the dynamic performance of transmission systems. This paper discusses the development of a genetic algorithm (GA) model to minimize the weighted sum of the magnitudes of gear mesh frequency components. The GA algorithm was designed based on a mathematical formulation for computing static transmission error and load sharing for low-contact-ratio external spur gears. The constructed GA was able to overcome the local optima and achieve global optimal/near-optimal solutions. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid architecture that combines correlation dimension and feature extraction techniques into the case-based reasoning (CBR) technique to do electrocardiogram (ECG) diagnosis. CBR is used in the system as the core problem-solving method, which applies the expert experience to analyze ECG and propose diagnosis. The correlation dimension values of ECGx are used for clustering of cases for CBR. A Min-Max Turning Points Selection (MTPS, algorithm is used to quickly extract turning points from an ECG waveform as wave features for retrieval of the most promising solution cases from a specific cluster of cases. Finally, the CBR can learn representative cases from the problem-solving process by an approximation-based training method. This hybrid architecture manifests several interesting features. First, high-quality representative cases can be effectively learned, thanks to the correlation dimension values-based clustering and approximation-based training methods. This feature not only reduces the number of cases needed to be stored in the case library, but improves the correctness of the proposed solutions. Second, the proposed MTPS algorithm for extraction of turning points is fast, simple and noise-tolerant, which helps a lot in the calculation of similarity among cases. Finally, the clustering algorithm can automatically re-cluster the cases in the case library in response to new diseases, which prevents our system from degradation due to the increase of new cases. Our experiments with the MIT-BIII arrhythmia database demonstrate that the architecture has very high sensitivity and specificity, as well as speedy response. [ABSTRACT FROM AUTHOR]

In this paper we study the resource management problem in Direct Sequence-Wideband Code Division Multiple Access (DS-WCDMA) systems. The control variables, transmission power, and transmission rate for resource management are considered. There meta-heuristic techniques. Genetic Algorithms (GA). Simulated Annealing (SA), and Tabu Search (TS), are utilized to solve this optimization problem. Also, a nonlinear programming technique. Generalized reduced Gradient (GRG) method, is adopted to compare with the three meta-heuristic techniques. Two approaches, the single objective approach. Multiobjective Genetic Algorithm (MOGA) is employed to compare with the other two well-known multiobjective evohitionary algorithms (MOEAs), Pareto Archived Evolution Strategy (PAES) and Micro-Genetic Algorithms (MICRO-GA). Two scenarios, scenario (a): 25 users and scenario (b): 50 users, are considered for both approaches. The simulation results of a single objective approach show that GA outperforms SA. TS. and GRG in the two scenarios. Also, the simulation results of a multiobjective approach show that MOGA outperforms PAES and MICRO-GA, and obtains nondominated trade-off solutions with better convergence and diversity between trade minimization of total power and maximization of total rate in two scenarios. [ABSTRACT FROM AUTHOR]

This paper presents the use of a cooperative co-evolutionary genetic algorithm (CCGA) in conjunction with a heuristic rule for solving a 3D container loading or bin packing problem. Unlike previous works, which concentrate on using either a heuristic rule or an optimization technique to find an optimal sequence of packages which must be loaded into the containers, the proposed heuristic rule is used to partition the entire loading sequence into a number of shorter sequences. Each partitioned sequence is then represented by a species member in the CCGA search. The simulation results indicate that the use of the heuristic rule and the CCGA is highly efficient in terms of the compactness of packages in comparison to the results given by a standard genetic algorithm search. In addition, this helps to confirm that the CCGA is also suitable for use in a sequence-based optimization problem. [ABSTRACT FROM AUTHOR]

This paper presents a new distributed algorithm based on Ant System (AS) concepts called combinatorial Ant System (CAS). It is oriented to solve static discrete-state combinatorial optimization problems. Our approach consists of mapping the solution space of the combinatorial optimization problem in the space where the ants will walk, and defining the transition probability and the pheromone update formula of the Ant System, according to the objective function of the combinatorial Optimization Problem. We test our approach on the graph partitioning, graph coloring and traveling salesman problems. [ABSTRACT FROM AUTHOR]

Accurate diagnosis and classification is the key issue for the optimal treatment of cancer patients. Several studies demonstrate that cancer classification can be estimated with high accuracy, sensitivity, and specificity from microarray-based gene expression profiling using artificial neural networks. In this paper, a comprehensive study was undertaken to investigate the capability of the probabilistic neural networks along with a feature selection method in the application of cancer classification. The feature selection method is based on the correlation with the class distinction. The experimental results show that the conjugation of the probabilistic neural network and the feature selection method can achieve 100% recognition accuracy' in the ALL/AML classification, and also attain satisfactory results in two colon cancer data sets. [ABSTRACT FROM AUTHOR]

Compression measures used in inductive learners, such as measures based on the minimum description length principle, can be used as a basis for grading candidate hypotheses. Compression-based induction is suited also for handling noisy data. This paper shows that a simple compression measure can be used to detect noisy training examples, where noise is due to random classification errors. A technique is proposed in which noisy examples are detected and eliminated from the training set, and a hypothesis is then built from the set of remaining examples. This noise elimination method was applied to preprocess data for four machine-learning algorithms, and evaluated on selected medical domains. [ABSTRACT FROM AUTHOR]