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In this paper, a new kind of swarm-based metaheuristic search method, called Elephant Herding Optimization (EHO), is proposed for solving optimization tasks. The EHO method is inspired by the herding behavior of elephant group. In nature, the elephants belonging to different clans live together under the leadership of a matriarch, and the male elephants will leave their family group when they grow...
In this paper electroencephalography (EEG) patterns are classified using a feedforward neural network trained with a modified genetic algorithm (GA). The objective is to investigate the effects of weight initialization in the neural network and to propose the best settings. Special operators like geometric ranking selection, blend-alpha crossover and non-uniform mutation are employed. For the initialization...
The purpose of this study is to analyze and improvethe solution of the facility layout problem following earlierapproaches by Karray et al. We briefly describe the facilitylayout problem, including a short literature review, comparedifferent solution methods and finally introduce a new solutionapproach combining fuzzy logic with a genetic algorithm (GA). In the end we compare results from both implementations...
Fixed-outline floorplanning is a hot issue in physical design, and it is more complicated than outline-free floorplanning since it considers the chip fixed-outline constraints. In this paper, an improved simulated annealing algorithm (ISA) is proposed to solve fixed-outline floorplanning. In case ISA encounters premature convergence, it randomly generates a new floorplan which is independent of the...
SMOTE (Synthetic minority over-sampling technique) is a commonly used over-sampling technique to subside the imbalanced dataset problem. Traditionally SMOTE has two key important parameters, one is to control the amount of over-sampling, and the other specifies the area of the nearest neighbors. These two parameters are arbitrarily chosen by user. So there are no universally best default values. In...
This work proposes a novel preference based evolutionary multi and many-objective optimization approach to search a specific region of the Pareto front. First, to know the overview of the entire Pareto front, the proposed approach roughly approximates it by using a representative MOEA/D with uniformly distributed weight vectors. Then, the obtained solutions are plotted on the parallel coordinates...
When krill herd (KH) is used to solve complicated multimodal functions, sometimes it fails to find the best solutions and cannot converge fast. Herein, we propose a hybrid KH method, called PBILKH, by integrating the KH with the population-based incremental learning (PBIL). In addition, a type of elitism is applied to memorize the krill with the best fitness when finding the best solution. The effectiveness...
Named Entity Recognition or NER is one of the sub-research field of Information Extraction which can be used for machine translation, question answering, semantic web, etc. One of the biggest challenge of NER is the adversity to construct a manually labeled training data. In this work, we present a semi-supervised approach for Indonesian language NER which is capable of creating high quality training...
The Permutation Flow Shop scheduling Problem (PFSP) is a typical combinatorial optimization problem. In order to improve the efficacy in solving the PFSP, we applied a discrete mechanism to convert the real value of individuals into discrete job sequences at first. In particular, a Chaos-based Firefly Algorithm (CFA) is used to optimize the initial population, which provided a superior initial environment...
This paper is an experiment of data fruit plants comes from the Department of Food Crops and Horticultural Central of Aceh District and Bener Meriah District. We focused our experiment in using visualizing. The analysis contained in this paper have used AdaBoost.M1 to build the model classification and Mosaics plot for visualization. Using visualization with Mosaic plot we can see and depict some...
Feature subset selection is an important problem in machine learning and data mining. If the suitable features are selected, the results of classification or prediction will be more accurate, while if the unsuitable features are used, the results may have no meaningful. This paper presents a method for feature subset selection that uses the ensemble technique to increase the efficiency of feature...
In the context of marketing, attribution is theprocess of quantifying the value of marketing activities relativeto the final outcome. It is a topic rapidly growing inimportance as acknowledged by the industry. However, despitenumerous tools and techniques designed for its measurement, the absence of a comprehensive assessment and classificationscheme persists. Thus, we aim to bridge this gap by providingan...
The content-based image recognition is a research focus in the field of computer vision. Machine learning especially deep learning has a great potential in the field of image recognition. This paper adopts the support vector machine algorithm and deep learning method convolutional neural network to recognize books in the digital image library and compares their performance. Experiments show that both...
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