2017 IEEE Congress on Evolutionary Computation (CEC)最新文献_第10页

Multi-objective endmember extraction for hyperspectral images 高光谱图像的多目标端元提取

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969347

Hao Li, Jingjing Ma, Jia Liu, Maoguo Gong, Mingyang Zhang

引用次数: 4

Vector representation of non-standard spellings using dynamic time warping and a denoising autoencoder 使用动态时间扭曲和去噪自动编码器的非标准拼写的矢量表示

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-01 DOI: 10.1109/CEC.2017.7969473

M. B. Lazreg, M. G. Olsen, Ole-Christoffer Granmo

引用次数: 5

On the evolution of bent (n, m) functions 关于弯曲(n, m)函数的演化

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-01 DOI: 10.1109/CEC.2017.7969563

S. Picek, Karlo Knezevic, D. Jakobović

引用次数: 3

A comparison of probabilistic-based optimization approaches for vehicle routing problems 基于概率的车辆路径优化方法比较

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-01 DOI: 10.1109/CEC.2017.7969622

Roberto Santana, G. Sirbiladze, B. Ghvaberidze, Bidzina Matsaberidze

引用次数: 3

Short-term traffic flow prediction with optimized Multi-kernel Support Vector Machine 基于优化多核支持向量机的短期交通流预测

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-01 DOI: 10.1109/CEC.2017.7969326

Xianyao Ling, Xinxin Feng, Zhonghui Chen, Yiwen Xu, Haifeng Zheng

引用次数: 29

Search-based requirements traceability recovery: A multi-objective approach 基于搜索的需求跟踪恢复:一种多目标方法

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-01 DOI: 10.1109/CEC.2017.7969440

Adnane Ghannem, M. Hamdi, M. Kessentini, H. Ammar

引用次数: 7

Using fuzzy automata to diagnose and predict heart problems 利用模糊自动机诊断和预测心脏问题

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-01 DOI: 10.1109/CEC.2017.7969397

Maria Azahara Camacho-Magrinan, Mercedes G. Merayo, M. Núñez

引用次数: 5

Coral Reef Optimization for intensity-based medical image registration 基于强度的医学图像配准珊瑚礁优化

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-01 DOI: 10.1109/CEC.2017.7969357

E. Bermejo, M. Chica, S. Salcedo-Sanz, O. Cordón

{"title":"Coral Reef Optimization for intensity-based medical image registration","authors":"E. Bermejo, M. Chica, S. Salcedo-Sanz, O. Cordón","doi":"10.1109/CEC.2017.7969357","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969357","url":null,"abstract":"Image registration (IR) is an extended and important problem in computer vision. It involves the transformation of different sets of image data having a shared content into a common coordinate system. Specifically, we will deal with the 3D intensity-based medical IR problem where the intensity distribution of the images is considered, one of the most complex and time consuming variants. The limitations of traditional IR methods have boomed the application of evolutionary and metaheuristic-based approaches to solve the problem, aiming to improve the performance of existing methods both in terms of accuracy and efficiency. In this contribution, we consider the use of a recently proposed bio-inspired meta-heuristic: the Coral Reef Optimization Algorithm (CRO). This novel algorithm simulates the natural phenomena underlying a coral reef, where different corals grow, reproduce and fight with other corals for space in the colony. CRO has recently obtained promising results in different real-world applications and we think its operation mode can properly cope with the 3D intensity-based medical IR problem. We adapt the algorithm to the real-coding problem nature and run an experimental setup tackling sixteen real-world problem instances. The new proposal is benchmarked with recent, state-of-the-art IR techniques. The results show that the CRO-based overcomes the state-of-the-art results in terms of its robustness and time efficiency.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128457971","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: 4

A study on auto-configuration of Multi-Objective Particle Swarm Optimization Algorithm 多目标粒子群优化算法的自配置研究

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-01 DOI: 10.1109/CEC.2017.7969381

Ricardo H. R. Lima, A. Pozo

{"title":"A study on auto-configuration of Multi-Objective Particle Swarm Optimization Algorithm","authors":"Ricardo H. R. Lima, A. Pozo","doi":"10.1109/CEC.2017.7969381","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969381","url":null,"abstract":"Researches point out to the importance of automatic design of multi-objective evolutionary algorithms. Because in general, algorithms automatically designed outperform traditional multi-objective evolutionary algorithms from the literature. Nevertheless, until fairly recently, most of the researches have been focused on a small group of algorithms, often based on evolutionary algorithms. On the other hand, mono-objective Particle Swarm Optimization algorithm (PSO) have been widely used due to its flexibility and competitive results in different applications. Besides, as PSO performance depends on different aspects of design like the velocity equation, its automatic design has been targeted by many researches with encouraging results. Motivated by these issues, this work studies the automatic design of Multi-Objective Particle Swarm Optimization (MOPSO). A framework that uses a context-free grammar to guide the design of the algorithms is implemented. The framework includes a set of parameters and components of different MOPSOs, and two design algorithms: Grammatical Evolution (GE) and Iterated Racing (IRACE). Evaluation results are presented, comparing MOPSOs generated by both design algorithms. Furthermore, the generated MOPSOs are compared to the Speed-constrained MOPSO (SMPSO), a well-known algorithm using a set of Multi-Objective problems, quality indicators and statistical tests.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128666362","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: 6

A comprehensive survey of brain storm optimization algorithms 头脑风暴优化算法的综合调查

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-01 DOI: 10.1109/CEC.2017.7969498

Shi Cheng, Yifei Sun, Junfeng Chen, Quande Qin, Xianghua Chu, Xiu-juan Lei, Yuhui Shi

引用次数: 31