2015 IEEE Congress on Evolutionary Computation (CEC)最新文献_第4页

Solving CEC 2015 multi-modal competition problems using neighborhood based speciation differential evolution 基于邻域的物种差异进化求解CEC 2015多模态竞争问题

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257291

B. Qu, Jing J. Liang, Z. Wang, D. M. Liu

引用次数: 1

Nonlinear equation systems solved by many-objective Hype 用多目标Hype求解非线性方程组

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257222

Sha Qin, Sanyou Zeng, Wei Dong, Xi Li

引用次数: 14

An artificial bee colony algorithm with a memory scheme for dynamic optimization problems 动态优化问题的一种具有记忆方案的人工蜂群算法

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257217

H. Nakano, Masataka Kojima, A. Miyauchi

引用次数: 17

Ring optimization with extinction 消光环优化

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257040

D. Ashlock, Sierra Gillis, G. Fogel

引用次数: 4

Mining group stock portfolio by using grouping genetic algorithms 利用分组遗传算法挖掘组股组合

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7256964

Chun-Hao Chen, Cheng-Bon Lin, Chao-Chun Chen

引用次数: 17

Bacterial Foraging Optimization for intensity-based medical image registration 基于强度的医学图像配准细菌觅食优化

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257187

E. Bermejo, A. Valsecchi, S. Damas, O. Cordón

引用次数: 5

Evolutionary semi-supervised ordinal regression using weighted kernel Fisher discriminant analysis 基于加权核Fisher判别分析的进化半监督有序回归

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257300

Yuzhou Wu, Yu Sun, Xinle Liang, K. Tang, Zixing Cai

{"title":"Evolutionary semi-supervised ordinal regression using weighted kernel Fisher discriminant analysis","authors":"Yuzhou Wu, Yu Sun, Xinle Liang, K. Tang, Zixing Cai","doi":"10.1109/CEC.2015.7257300","DOIUrl":"https://doi.org/10.1109/CEC.2015.7257300","url":null,"abstract":"Ordinal regression has a wide range of applications, while it is intractable to be solved when lacking sufficient labeled data. In this paper, we propose an evolutionary semi-supervised kernel Fisher discriminant approach for ordinal regression. The proposed algorithm obtains the projection and thresholds by incorporating the unlabeled data with a weighting scheme, where the weights indicate the degrees of contributions to the class distribution by different training instances. The projection maps the original data to a one-dimensional space, and the thresholds are used for the prediction. The weights are computed with a label propagation method first. However, it is not always accurate. In order to further tune the weights to be more accurate, the differential evolution algorithm is applied here in this work. By a delicate weight update rule, the weights can be evolved indirectly. This tuning scheme makes the size of evolutionary individual just associated with the number of ranks rather than the number of instances. The experimental studies demonstrate that our algorithm can effectively use unlabeled data and yield satisfactory learning performance.","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"16 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":"127932945","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: 2

A double swarm methodology for parameter estimation in oscillating Gene Regulatory Networks 振荡基因调控网络参数估计的双群方法

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257179

Marco S. Nobile, H. Iba

{"title":"A double swarm methodology for parameter estimation in oscillating Gene Regulatory Networks","authors":"Marco S. Nobile, H. Iba","doi":"10.1109/CEC.2015.7257179","DOIUrl":"https://doi.org/10.1109/CEC.2015.7257179","url":null,"abstract":"S-systems are mathematical models based on the power-law formalism, which are widely employed for the investigation of Gene Regulatory Networks (GRNs). Because of their complex dynamics - characterized by multi-modality and nonlinearity-the parameterization of S-systems is far from straightforward, demanding global optimization techniques. The problem of parameter estimation of S-systems is further complicated when the desired dynamics is characterized by oscillations. In this work, we describe a novel methodology based on Particle Swarm Optimization for the automatic parameterization of oscillating Ssystems. In this methodology, two swarms perform independent optimizations, and cooperate by periodically exchanging the best particles. The two swarms exploit two different fitness functions: a traditional point-to-point distance, and a spectra-based fitness function. We show that this cooperative approach allows the double swarm to outperform the common methodology, based on a single swarm exploiting a single fitness function. We demonstrate the effectiveness of our method using a GRN of five genes, performing tests of increasing complexity, up to the simultaneous inference of 17 parameters.","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"360 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":"115902601","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

Deep community detection based on memetic algorithm 基于模因算法的深度社区检测

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7256952

Shanfeng Wang, Maoguo Gong, Bo Shen, Zhao Wang, Qing Cai, L. Jiao

引用次数: 4

Interactive differential evolution using time information required for user's selection: In a case of optimizing fragrance composition 使用用户选择所需的时间信息的交互式差分进化:在优化香水成分的情况下

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257155

M. Fukumoto, M. Inoue, Shimpei Koga, Jun-ichi Imai

{"title":"Interactive differential evolution using time information required for user's selection: In a case of optimizing fragrance composition","authors":"M. Fukumoto, M. Inoue, Shimpei Koga, Jun-ichi Imai","doi":"10.1109/CEC.2015.7257155","DOIUrl":"https://doi.org/10.1109/CEC.2015.7257155","url":null,"abstract":"Interactive Evolutionary Computation (IEC) spreads its area of applications. As an example of the applications, we have already proposed an IEC that optimizes fragrance composition suited to each user's feelings by using Interactive Differential Evolution (IDE). To create the fragrance, six aroma sources were mixed, and the intensity of them were target of optimization. Paired comparison was employed as subjective evaluation method. To enhance searching ability of the IDE method, this study proposes a new IDE utilizing both of evolution level of DE's vector (solution candidate) and time information required in user's selection in the paired comparison. In other words, shorter time duration required in the selection is considered as there is larger difference of fitness level between two vectors. Winner vector tended to have chance to evolve itself. These schemes in the proposed method are expected to accelerate searching better solutions. In this study, we constructed an IDE system optimizing fragrances based on the proposed method. Furthermore, with the IDE system, we conducted smelling experiments for investigating the fundamental efficiency of the proposed method. Target of creation was relaxing fragrance. The experimental results showed that obtained fragrances in the last generation was evaluated better than fragrances in initial generation significantly (P<;0.01).","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"38 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":"132564341","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