2012 4th Conference on Data Mining and Optimization (DMO)最新文献_第2页

Multiobjective genetic algorithm-based method for job shop scheduling problem: Machines under preventive and corrective maintenance activities 基于多目标遗传算法的作业车间调度问题:处于预防性和纠正性维修活动的机器

2012 4th Conference on Data Mining and Optimization (DMO) Pub Date : 2012-10-15 DOI: 10.1109/DMO.2012.6329791

Youssef Harrath, J. Kaabi, M. Sassi, M. Ali

{"title":"Multiobjective genetic algorithm-based method for job shop scheduling problem: Machines under preventive and corrective maintenance activities","authors":"Youssef Harrath, J. Kaabi, M. Sassi, M. Ali","doi":"10.1109/DMO.2012.6329791","DOIUrl":"https://doi.org/10.1109/DMO.2012.6329791","url":null,"abstract":"In this paper we consider a multiobjective job shop scheduling problem. The machines are subject to availability constraints that are due to preventive maintenance, machine breakdowns or tool replacement. Two optimization criteria were considered; the makespan for the jobs and the total cost for the maintenance activities. The job shop scheduling problem without considering the availability constraints is known to be NP-Hard. Because of the complexity of the problem, we develop a two-phase genetic algorithm based heuristic to solve the addressed problem. A set of pareto optimal solutions is obtained in the first phase containing relatively large number of solutions. This makes difficult the choice of the most suitable solution. For this reason the second phase will filter the obtained set so as to reduce its size. Performance of the proposed heuristic is evaluated through computational experiments on the benchmark of Muth & Thomson mt06 of 6×6 and 10 different sizes benchmarks of Lawrence. The results show that the heuristic gives solutions close to those obtained in the classic job shop scheduling problem.","PeriodicalId":330241,"journal":{"name":"2012 4th Conference on Data Mining and Optimization (DMO)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128044255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Spatial and temporal analysis of deforestation and forest degradation in Selangor: Implication to carbon stock above ground 雪兰莪州森林砍伐和森林退化的时空分析:对地上碳储量的影响

2012 4th Conference on Data Mining and Optimization (DMO) Pub Date : 2012-10-15 DOI: 10.1109/DMO.2012.6329789

Sharifah Mastura Syed Abdullah

引用次数: 2

Opposition based Particle Swarm Optimization with student T mutation (OSTPSO) 基于对立的学生T突变粒子群优化算法

2012 4th Conference on Data Mining and Optimization (DMO) Pub Date : 2012-10-15 DOI: 10.1109/DMO.2012.6329802

M. Imran, R. Hashim, Noor Elaiza Abd Khalid

引用次数: 10

Evolutionary-based feature construction with substitution for data summarization using DARA 基于进化的特征构建替代了基于DARA的数据摘要

2012 4th Conference on Data Mining and Optimization (DMO) Pub Date : 2012-10-15 DOI: 10.1109/DMO.2012.6329798

F. Sia, R. Alfred

{"title":"Evolutionary-based feature construction with substitution for data summarization using DARA","authors":"F. Sia, R. Alfred","doi":"10.1109/DMO.2012.6329798","DOIUrl":"https://doi.org/10.1109/DMO.2012.6329798","url":null,"abstract":"The representation of input data set is important for learning task. In data summarization, the representation of the multi-instances stored in non-target tables that have many-to-one relationship with record stored in target table influences the descriptive accuracy of the summarized data. If the summarized data is fed into a classifier as one of the input features, the predictive accuracy of the classifier will also be affected. This paper proposes an evolutionary-based feature construction approach namely Fixed-Length Feature Construction with Substitution (FLFCWS) to address the problem by means of optimizing the feature construction for relational data summarization. This approach allows initial features to be used more than once in constructing newly constructed features. This is performed in order to exploit all possible interactions among attributes which involves an application of genetic algorithm to find a relevant set of features. The constructed features will be used to generate relevant patterns that characterize non-target records associated to the target record as an input representation for data summarization process. Several feature scoring measures are used as fitness function to find the best set of constructed features. The experimental results show that there is an improvement of predictive accuracy for classifying data summarized based on FLFCWS approach which indirectly improves the descriptive accuracy of the summarized data. It shows that FLFCWS approach can generate promising set of constructed features to describe the characteristics of non-target records for data summarization.","PeriodicalId":330241,"journal":{"name":"2012 4th Conference on Data Mining and Optimization (DMO)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130959261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

The effect of learning mechanism in Variables Neighborhood Search 学习机制对变量邻域搜索的影响

2012 4th Conference on Data Mining and Optimization (DMO) Pub Date : 2012-10-15 DOI: 10.1109/DMO.2012.6329807

R. Aziz, M. Ayob, Z. Othman

{"title":"The effect of learning mechanism in Variables Neighborhood Search","authors":"R. Aziz, M. Ayob, Z. Othman","doi":"10.1109/DMO.2012.6329807","DOIUrl":"https://doi.org/10.1109/DMO.2012.6329807","url":null,"abstract":"The basic idea of the Variable Neighborhood Search (VNS) algorithm is to systematically explore the neighborhood of current solution using a set of predefined neighborhood structures. Since different problem instances have different landscape and complexity, the choice of which neighborhood structure to be applied is a challenging task. Different neighborhood structures may lead to different solution space. Therefore, this work proposes a learning mechanism in a Variable Neighborhood Search (VNS), refer to hereafter as a Variable Neighborhood Guided Search (VNGS). Its effectiveness is illustrated by solving a course timetabling problems. The learning mechanism memorizes which neighborhood structure could effectively solve a specific soft constraint violations and used it to guide the selection of neighborhood structure to enhance the quality of a best solution. The performance of the VNGS is tested over Socha course timetabling dataset. Results demonstrate that the performance of the VNGS is comparable with the results of the other VNS variants and outperformed others in some instances. This demonstrates the effectiveness of applying a learning mechanism in a VNS algorithm.","PeriodicalId":330241,"journal":{"name":"2012 4th Conference on Data Mining and Optimization (DMO)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123198640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Edge preserving image enhancement via harmony search algorithm 基于和谐搜索算法的图像边缘保持增强

2012 4th Conference on Data Mining and Optimization (DMO) Pub Date : 2012-10-15 DOI: 10.1109/DMO.2012.6329797

Zaid Abdi Alkareem, Ibrahim Venkat, M. Al-Betar, A. Khader

引用次数: 25

A hybrid model using genetic algorithm and neural network for predicting dengue outbreak 基于遗传算法和神经网络的登革热疫情预测混合模型

2012 4th Conference on Data Mining and Optimization (DMO) Pub Date : 2012-10-15 DOI: 10.1109/DMO.2012.6329793

N. Husin, N. Mustapha, M. N. Sulaiman, R. Yaakob

{"title":"A hybrid model using genetic algorithm and neural network for predicting dengue outbreak","authors":"N. Husin, N. Mustapha, M. N. Sulaiman, R. Yaakob","doi":"10.1109/DMO.2012.6329793","DOIUrl":"https://doi.org/10.1109/DMO.2012.6329793","url":null,"abstract":"Prediction of dengue outbreak becomes crucial in Malaysia because this infectious disease remains one of the main health issues in the country. Malaysia has a good surveillance system but there have been insufficient findings on suitable model to predict future outbreaks. While there are previous studies on dengue prediction models in Malaysia, unfortunately some of these models still have constraints in finding good parameter with high accuracy. The aim of this paper is to design a more promising model for predicting dengue outbreak by using a hybrid model based on genetic algorithm for the determination of weight in neural network model. Several model architectures are designed and the parameters are adjusted to achieve optimal prediction performance. Sample data that covers dengue and rainfall data of five districts in Selangor collected from State Health Department of Selangor (SHD) and Malaysian Meteorological Department is used as a case study to evaluate the proposed model. However, due to incomplete collection of real data, a sample data with similar behavior was created for the purpose of preliminary experiment. The result shows that the hybrid model produces the better prediction compared to standalone models.","PeriodicalId":330241,"journal":{"name":"2012 4th Conference on Data Mining and Optimization (DMO)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124640262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

Web crawler with URL signature — A performance study 带有URL签名的网络爬虫-性能研究

2012 4th Conference on Data Mining and Optimization (DMO) Pub Date : 2012-10-15 DOI: 10.1109/DMO.2012.6329810

Lay-Ki Soon, Yee-Ern Ku, Sang Ho Lee

引用次数: 5

Fuzzy rule-based for predicting machining performance for SNTR carbide in milling titanium alloy (Ti-6Al-4v) 基于模糊规则的SNTR硬质合金铣削钛合金(Ti-6Al-4v)加工性能预测

2012 4th Conference on Data Mining and Optimization (DMO) Pub Date : 2012-10-15 DOI: 10.1109/DMO.2012.6329803

M. Adnan, A. Zain, H. Haron

引用次数: 1

An algorithm for the selection of planting lining technique towards optimizing land Area: An algorithm for planting lining technique selection 面向土地面积优化的种植衬砌技术选择算法

2012 4th Conference on Data Mining and Optimization (DMO) Pub Date : 2012-10-15 DOI: 10.1109/DMO.2012.6329794

Ismadi Badarudin, Abu Bakar Md Sultan, Md Nasir Sulaiman, Ali Mamat, M. Mohamed

引用次数: 1