2017 IEEE Congress on Evolutionary Computation (CEC)最新文献_第2页

Enhanced firefly algorithm for constrained numerical optimization 约束数值优化的改进萤火虫算法

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969561

I. Strumberger, N. Bačanin, M. Tuba

引用次数: 40

Particle filter track-before-detect algorithm with Lamarckian inheritance for improved dim target tracking 基于lamarkian遗传的粒子滤波检测前跟踪算法改进了弱小目标跟踪

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969437

Lin Li, Yun Li

引用次数: 4

A first attempt on global evolutionary undersampling for imbalanced big data 对不平衡大数据的全球进化欠采样的首次尝试

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969553

I. Triguero, M. Galar, H. Bustince, F. Herrera

{"title":"A first attempt on global evolutionary undersampling for imbalanced big data","authors":"I. Triguero, M. Galar, H. Bustince, F. Herrera","doi":"10.1109/CEC.2017.7969553","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969553","url":null,"abstract":"The design of efficient big data learning models has become a common need in a great number of applications. The massive amounts of available data may hinder the use of traditional data mining techniques, especially when evolutionary algorithms are involved as a key step. Existing solutions typically follow a divide-and-conquer approach in which the data is split into several chunks that are addressed individually. Next, the partial knowledge acquired from every slice of data is aggregated in multiple ways to solve the entire problem. However, these approaches are missing a global view of the data as a whole, which may result in less accurate models. In this work we carry out a first attempt on the design of a global evolutionary undersampling model for imbalanced classification problems. These are characterised by having a highly skewed distribution of classes in which evolutionary models are being used to balance the dataset by selecting only the most relevant data. Using Apache Spark as big data technology, we have introduced a number of variations to the well-known CHC algorithm to work with very large chromosomes and reduce the costs associated to the fitness evaluation. We discuss some preliminary results, showing the great potential of this new kind of evolutionary big data model.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"676 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121996429","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

Genetic programming for improved cryptanalysis of elliptic curve cryptosystems 改进椭圆曲线密码系统密码分析的遗传规划

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969342

Tim Ribaric, S. Houghten

引用次数: 5

LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems 半参数自适应LSHADE与CMA-ES混合求解CEC 2017基准问题

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969307

A. W. Mohamed, Anas A. Hadi, A. Fattouh, K. Jambi

{"title":"LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems","authors":"A. W. Mohamed, Anas A. Hadi, A. Fattouh, K. Jambi","doi":"10.1109/CEC.2017.7969307","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969307","url":null,"abstract":"To improve the optimization performance of LSHADE algorithm, an alternative adaptation approach for the selection of control parameters is proposed. The proposed algorithm, named LSHADE-SPA, uses a new semi-parameter adaptation approach to effectively adapt the values of the scaling factor of the Differential evolution algorithm. The proposed approach consists of two different settings for two control parameters F and Cr. The benefit of this approach is to prove that the semi-adaptive algorithm is better than pure random algorithm or fully adaptive or self-adaptive algorithm. To enhance the performance of our algorithm, we also introduced a hybridization framework named LSHADE-SPACMA between LSHADE-SPA and a modified version of CMA-ES. The modified version of CMA-ES undergoes the crossover operation to improve the exploration capability of the proposed framework. In LSHADE-SPACMA both algorithms will work simultaneously on the same population, but more populations will be assigned gradually to the better performance algorithm. In order to verify and analyze the performance of both LSHADE-SPA and LSHADE-SPACMA, Numerical experiments on a set of 30 test problems from the CEC2017 benchmark for 10, 30, 50 and 100 dimensions, including a comparison with LSHADE algorithm are executed. Experimental results indicate that in terms of robustness, stability, and quality of the solution obtained, of both LSHADE-SPA and LSHADE-SPACMA are better than LSHADE algorithm, especially as the dimension increases.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130549889","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: 223

An evolutionary trust game for the sharing economy 共享经济的进化信任游戏

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969610

M. Chica, R. Chiong, M. Adam, S. Damas, Timm Teubner

引用次数: 14

A closer look to elitism in ε-dominance many-objective optimization ε-显性多目标优化中的精英主义

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969638

Ryoma Sano, H. Aguirre, Kiyoshi Tanaka

引用次数: 0

An improved multiobjective evolutionary approach for community detection in multilayer networks 一种改进的多层网络社区检测的多目标进化方法

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969345

Wenfeng Liu, Shanfeng Wang, Maoguo Gong, Mingyang Zhang

引用次数: 5

Single objective real-parameter optimization: Algorithm jSO 单目标实参数优化:jSO算法

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969456

J. Brest, M. Maučec, B. Bošković

引用次数: 286

Multi-agent systems applied to power loss minimization in distribution-level smart grid with dynamic load variation 多智能体系统在负荷动态变化的配电级智能电网中的应用

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969316

F. Saraiva, L. Nordström, E. Asada

{"title":"Multi-agent systems applied to power loss minimization in distribution-level smart grid with dynamic load variation","authors":"F. Saraiva, L. Nordström, E. Asada","doi":"10.1109/CEC.2017.7969316","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969316","url":null,"abstract":"Modern distribution system are expected to provide new features such as taking advantage of Cyber-Physical Systems (CPS) - new equipment and devices embedded with sensors, network communication, and computational intelligence techniques to provide increased system performance and power quality. Among the performance improvement, the reduction of electrical losses is an important quality factor which is associated with energy efficiency. This paper presents a method based on Multi-agent Systems (MAS) that manages topology changes by switching operations to improve the system performance in dynamic scenario, where the power demand varies throughout the day. Experiments were performed allocating three different load consumer profiles (residential, commercial, and industrial) in two test systems with 12-bus and 16-bus, creating several scenarios. The agents were deployed in a set of small-sized single-board computers with low computational power to mimic CPS. The simulations has shown the success of the method on managing the decision making among different agents to provide the joint effort to manage the loss reduction on the network.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116714208","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: 8