2013 IEEE Congress on Evolutionary Computation最新文献_第7页

Supergenes in a genetic algorithm for heterogeneous FPGA placement 异构FPGA放置的遗传算法中的超基因

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557578

P. Jamieson, Farnaz Gharibian, Lesley Shannon

引用次数: 9

Tackling the Irregular Strip Packing problem by hybridizing genetic algorithm and bottom-left heuristic 用混合遗传算法和左下启发式算法求解不规则条形包装问题

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557936

B. A. Júnior, P. Pinheiro, R. D. Saraiva

引用次数: 9

PSO neural inverse optimal control for a linear induction motor 线性感应电动机的粒子群神经逆最优控制

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557801

V. Lopez, E. Sánchez, A. Alanis

引用次数: 3

GPF-CLASS: A Genetic Fuzzy model for classification GPF-CLASS:一种遗传模糊分类模型

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557971

Adriano Soares Koshiyama, Tatiana Escovedo, D. Dias, M. Vellasco, R. Tanscheit

引用次数: 11

Behavioral diversity with multiple behavioral distances 具有多重行为距离的行为多样性

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557731

S. Doncieux, Jean-Baptiste Mouret

引用次数: 40

Evolutionary hybrid computation in view of design information by data mining 基于数据挖掘的设计信息进化混合计算

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557985

Kazuhisa Chiba

{"title":"Evolutionary hybrid computation in view of design information by data mining","authors":"Kazuhisa Chiba","doi":"10.1109/CEC.2013.6557985","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557985","url":null,"abstract":"Design Informatics has three points of view. First point is the efficient exploration in design space using evolutionary computation. Second point is the structurization and visualization of design space using data mining. Third point is the application to practical problems. In the present study, the influence of the seven pure and hybrid optimizers for design information has been investigated in order to explain the selection manner of optimizer for data mining. A single-stage hybrid rocket design problem is picked up as the present design object. As a result, mining result depends on not the number of generation (convergence) but the optimizers (diversity). Consequently, the optimizer with diversity performance should be selected in order to obtain global design information in the design space. Therefore, the diversity performance has also been explained for the seven optimization methods by using three standard mathematical test problems with/without noise. The result indicates that the hybrid method between the differential evolution and the genetic algorithm is beneficial performance for efficient exploration in the design space under the condition for large-scale design problems within 102 order evolution at most. Moreover, the comparison among eight crossovers indicates that the principal component analysis blended crossover is good selection on the hybrid method between the differential evolution and the genetic algorithm.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129084851","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

Cost minimization of service deployment in a multi-cloud environment 在多云环境中实现服务部署成本最小化

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557880

Francois Legillon, N. Melab, Didier Renard, E. Talbi

引用次数: 18

An Ant Colony System algorithm for automatically schematizing transport network data sets 基于蚁群算法的运输网络数据集自动图化

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557790

M. Ware, Nigel Richards

引用次数: 9

An evolutionary algorithm for Feature Selective Double Clustering of text documents 文本文档特征选择双聚类的进化算法

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557603

Seyednaser Nourashrafeddin, E. Milios, D. Arnold

引用次数: 3

A coevolutionary algorithm to automatic test case selection and mutant in Mutation Testing 突变测试中自动选择测试用例和突变的协同进化算法

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557654

André Assis Lôbo de Oliveira, C. Camilo-Junior, A. Vincenzi

{"title":"A coevolutionary algorithm to automatic test case selection and mutant in Mutation Testing","authors":"André Assis Lôbo de Oliveira, C. Camilo-Junior, A. Vincenzi","doi":"10.1109/CEC.2013.6557654","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557654","url":null,"abstract":"One of the main problems to perform the Software Testing is to find a set of tests (subset from input domain of the problem) which is effective to detect the remaining bugs in the software. The Search-Based Software Testing (SBST) approach uses metaheuristics to find low cost set of tests with a high effectiveness to detect bugs. From several existing test criteria, Mutation Testing is considered quite promising to reveal bugs, despite its high computational cost, due to the great quantity of mutant programs generated. Therefore, this paper addresses the problem of selecting mutant programs and test cases in Mutation Testing context. To this end, it is proposed a Coevolutionary Genetic Algorithm (CGA) and the concept of Genetic Effectiveness, describing a new representation and implementing new genetic operators. The CGA is applied in five benchmarks and the results are compared to other five methods, showing a better performance of the proposed algorithm in subsets automatic selection with better mutation score and greater reduction of computational cost, specifically the amount of testing, when compared with exhaustive test.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"224 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123255947","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: 42