2015 IEEE Congress on Evolutionary Computation (CEC)最新文献_第5页

Population-based optimization having deterministic and discrete dynamics 具有确定性和离散动力学的基于种群的优化

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257216

Yuya Kurita, T. Tsubone

引用次数: 0

Multi-objective evolutionary optimization of evasive maneuvers including observability performance 包含可观察性性能的规避机动多目标进化优化

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7256946

Yu Dateng, Ya-zhong Luo, Jiang Zicheng, G. Tang

引用次数: 1

Annealing linear scalarized based multi-objective multi-armed bandit algorithm 基于退火线性标化的多目标多臂强盗算法

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257097

Saba Q. Yahyaa, Mădălina M. Drugan, B. Manderick

引用次数: 1

A study on multi-objective particle swarm model by personal archives with regular graph 基于正则图的个人档案多目标粒子群模型研究

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257221

T. Uchitane, T. Hatanaka

引用次数: 2

Memetic firefly algorithm for data fitting with rational curves 有理曲线数据拟合的模因萤火虫算法

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7256932

A. Iglesias, A. Gálvez

{"title":"Memetic firefly algorithm for data fitting with rational curves","authors":"A. Iglesias, A. Gálvez","doi":"10.1109/CEC.2015.7256932","DOIUrl":"https://doi.org/10.1109/CEC.2015.7256932","url":null,"abstract":"This paper concerns the problem of obtaining a smooth fitting curve to a given set of (noisy) data points. This problem arises frequently in several industrial fields, such as computer-aided design and manufacturing (construction of car bodies, ship hulls, airplane fuselage), computer graphics and animation, medicine, and many others. The classical approach relies on polynomial functions to solve this problem. It has been noticed, however, that some shapes cannot be properly approximated through this polynomial scheme. In this paper, we address this issue by using rational functions, particularly the rational Bernstein basis functions. This poses an additional challenge: we have not only to compute the poles of the resulting rational Bézier fitting curve but also to obtain their corresponding weights and a suitable parameterization of data points. Overall, this leads to a continuous multivariate nonlinear optimization problem that cannot be solved through traditional mathematical optimization techniques. Our approach to tackle this issue is based on a memetic firefly algorithm combining a powerful metaheuristic technique (the firefly algorithm) for global optimization with a local search method. The performance of our scheme is illustrated through its application to four illustrative examples of free-form synthetic shapes. Our experimental results show that our memetic approach performs very well, and allows us to reconstruct the underlying shape of data points automatically with high accuracy. A comparative analysis on our benchmark shows that our approach outperforms some alternative methods reported in the literature for this problem.","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132384388","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: 13

Particle filter with extrapolation by crossover for nonlinear state estimation 非线性状态估计的交叉外推粒子滤波

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257201

Taku Sasaki, I. Ono

{"title":"Particle filter with extrapolation by crossover for nonlinear state estimation","authors":"Taku Sasaki, I. Ono","doi":"10.1109/CEC.2015.7257201","DOIUrl":"https://doi.org/10.1109/CEC.2015.7257201","url":null,"abstract":"This paper proposes a new particle filter (PF) named the particle filter with extrapolation by crossover (PF-XC) for estimating state vectors of dynamical systems. Estimating state vectors of dynamical systems is one of the most important problems that often appears in the wide area of engineering such as robotics, statistics and marine meteorology. The particle filter with interpolation by crossover (PF-IC) is one of the most promising PFs that overcomes a problem of the original PF. PF-IC interpolates particles to obtain an ensemble with high density around the true state. PF-IC shows better performance than PF especially when the number of particles in an ensemble is small. However, PF-IC has a serious problem in that the performance of PF-IC deteriorates when the ensemble does not cover the true state. We believe that this is because PF-IC cannot create particles around the true state when the ensemble does not cover the true state. In order to remedy the problem of PF-IC, PF-XC extrapolates particles to obtain an expanded ensemble in an isotropic manner that covers the true state. In order to investigate that PF-XC effectively works even if ensembles do not cover true states, we compared the performance of PF-XC and that of PF-IC, PF and the merging particle filter (MPF) which is one of the most famous extensions of PF on two benchmark problems that have nonlinear dynamics models. As the result, we confirmed that PF-XC outperformed PF-IC, PF and MPF. PF-XC showed up to about eight times better performance than that of PF-IC in terms of the median root mean squared error.","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128868548","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

Impact of imputation of missing values on genetic programming based multiple feature construction for classification 缺失值输入对基于遗传规划的多特征分类构建的影响

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257182

Cao Truong Tran, Peter M. Andreae, Mengjie Zhang

{"title":"Impact of imputation of missing values on genetic programming based multiple feature construction for classification","authors":"Cao Truong Tran, Peter M. Andreae, Mengjie Zhang","doi":"10.1109/CEC.2015.7257182","DOIUrl":"https://doi.org/10.1109/CEC.2015.7257182","url":null,"abstract":"Missing values are a common problem in many real world databases. A common way to cope with this problem is to use imputation methods to fill missing values with plausible values. Genetic programming-based multiple feature construction (GPMFC) is a filter approach to multiple feature construction for classifiers using Genetic programming. The GPMFC algorithm has been demonstrated to improve classification performance in decision tree and rule-based classifiers for complete data, but it has not been tested on imputed data. This paper studies the effect of GPMFC on classification accuracy with imputed data and how the choice of different imputation methods (mean imputation, hot deck imputation, Knn imputation, EM imputation and MICE imputation) affects classifiers using constructed features. Results show that GPMFC improves classification performance for datasets with a small amount of missing values. The combination of GPMFC and MICE imputation, in most cases, enhances classification performance for datasets with varying amounts of missing values and obtains the best classification accuracy.","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133725685","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: 12

An exploratory path planning method based on genetic algorithm for autonomous mobile robots 基于遗传算法的自主移动机器人探索性路径规划方法

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7256875

V. Santos, C. Toledo, F. Osório

{"title":"An exploratory path planning method based on genetic algorithm for autonomous mobile robots","authors":"V. Santos, C. Toledo, F. Osório","doi":"10.1109/CEC.2015.7256875","DOIUrl":"https://doi.org/10.1109/CEC.2015.7256875","url":null,"abstract":"The path planning task for mobile robots consists of define a trajectory to the robot leaves its start position and reach its goal without to collide with obstacles. In general, the robot needs to know previous information about the environment (e.g. maps, predefined routes) to plan its trajectory. In an exploration task, the robot does not know the environment and discovers it when moving to reach the goal coordinates. In this paper, an exploratory path planning aiming to reach a goal position is studied and a new method based on genetic algorithm, topological environment representation and realistic robot actions is proposed. In this method, the robots execute a sequence of reliable local actions (simple reactive behaviors) to move through the unknown environment, adopting a topological environment representation. They plan the path at the same time the environment is explored, in which the genetic algorithm evolves the sequence of actions to be executed. The results show that the squad of robots (GA population) reach the goal faster than an individual search. The proposed approach deal with environment traps better than the classical search A* algorithm and a variation of the A*, named C*, here also introduced.","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115484494","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

Dynamic search fireworks algorithm with covariance mutation for solving the CEC 2015 learning based competition problems 基于协方差变异的动态搜索烟花算法求解CEC 2015学习竞争问题

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257013

Chao Yu, L. Kelley, Ying Tan

引用次数: 30

Towards optimal ship design and valuable knowledge discovery under uncertain conditions 不确定条件下船舶优化设计与有价值知识发现

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257107

K. Deb, Zhichao Lu, C. McKesson, C. Trumbach, L. DeCan

引用次数: 3