2013 IEEE Congress on Evolutionary Computation最新文献_第10页

A Genetic Programming based approach to automatically generate Wireless Sensor Networks applications 基于遗传规划的无线传感器网络应用程序自动生成方法

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557775

R. R. Oliveira, T. Heimfarth, R. W. Bettio, M. Arantes, C. Toledo

{"title":"A Genetic Programming based approach to automatically generate Wireless Sensor Networks applications","authors":"R. R. Oliveira, T. Heimfarth, R. W. Bettio, M. Arantes, C. Toledo","doi":"10.1109/CEC.2013.6557775","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557775","url":null,"abstract":"The development of Wireless Sensor Networks (WSNs) applications is an arduous task, since the application needs to be customized for each sensor. Thus, the automatic generation of WSN's applications is desirable to reduce costs, since it drastically reduces the human effort. This paper presents the use of Genetic Programming to automatically generate WSNs applications. A scripting language based on events and actions is proposed to represent the WSN behavior. Events represent the state of a given sensor node and actions modify these states. Some events are internal states and others are external states captured by the sensors. The genetic programming is used to automatically generate WSNs applications described using this scripting language. These scripts are executed by all network's sensors. This approach enables the application designer to define only the overall objective of the WSN. This objective is defined by means of a fitness function. An event-detection problem is presented in order to evaluate the proposed method. The results shown the capability of the developed approach to successfully solve WSNs problems through the automatic generation of applications.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"28 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":"131797121","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

Evolutionary medical image registration using automatic parameter tuning 采用自动参数调整的进化医学图像配准

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557718

A. Valsecchi, Jérémie Dubois-Lacoste, T. Stützle, S. Damas, J. Santamaría, L. Marrakchi-Kacem

引用次数: 12

Using good and bad diversity measures in the design of ensemble systems: A genetic algorithm approach 集成系统设计中好坏分集度量的应用:一种遗传算法方法

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557649

Antonino Feitosa Neto, A. Canuto, Teresa B Ludermir

引用次数: 4

Extending features for multilabel classification with swarm biclustering 用群双聚类扩展多标签分类的特征

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557930

R. Prati, F. O. França

引用次数: 9

A comparative study of dynamic resampling strategies for guided Evolutionary Multi-objective Optimization 导向进化多目标优化的动态重采样策略比较研究

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557782

Florian Siegmund, A. Ng, K. Deb

{"title":"A comparative study of dynamic resampling strategies for guided Evolutionary Multi-objective Optimization","authors":"Florian Siegmund, A. Ng, K. Deb","doi":"10.1109/CEC.2013.6557782","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557782","url":null,"abstract":"In Evolutionary Multi-objective Optimization many solutions have to be evaluated to provide the decision maker with a diverse choice of solutions along the Pareto-front, in particular for high-dimensional optimization problems. In Simulation-based Optimization the modeled systems are complex and require long simulation times. In addition the evaluated systems are often stochastic and reliable quality assessment of system configurations by resampling requires many simulation runs. As a countermeasure for the required high number of simulation runs caused by multiple optimization objectives the optimization can be focused on interesting parts of the Pareto-front, as it is done by the Reference point-guided NSGA-II algorithm (R-NSGA-II) [9]. The number of evaluations needed for the resampling of solutions can be reduced by intelligent resampling algorithms that allocate just as much sampling budget needed in different situations during the optimization run. In this paper we propose and compare resampling algorithms that support the R-NSGA-II algorithm on optimization problems with stochastic evaluation functions.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"49 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":"122994120","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: 22

Overcoming faults using evolution on the PAnDA architecture 利用熊猫架构上的进化来克服错误

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557625

Pedro B. Campos, David M. R. Lawson, S. Bale, James Alfred Walker, M. Trefzer, A. Tyrrell

引用次数: 8

Infeasibility driven approach for bi-objective evolutionary optimization 双目标进化优化的非可行性驱动方法

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557659

D. Sharma, Prem Soren

引用次数: 6

The Parameter-less Evolutionary Search for real-parameter single objective optimization 实参数单目标优化的无参数进化搜索

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557693

G. Papa, J. Silc

{"title":"The Parameter-less Evolutionary Search for real-parameter single objective optimization","authors":"G. Papa, J. Silc","doi":"10.1109/CEC.2013.6557693","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557693","url":null,"abstract":"A parameter-less algorithm allows optimal solutions to be found without the need for setting the control parameters. Namely, finding an appropriate parameter setup for an evolutionary algorithm is a challenging research problem, and the setup optimality is crucial for algorithm's good performance. Therefore, the approaches that are able to solve any problem without any human intervention to set suitable control parameters are particulary interesting. The Parameterless Evolutionary Search (PLES) algorithm, with its real-value and combinatorial version, is based on a basic genetic algorithm, but it does not need any control parameter to be set in advance. It is able to find optimal, or at least very good, solutions relatively quickly, and without the need for a parameter-setting specialist. The last of these is a very important issue when used by engineers that do not have a detailed background knowledge: neither about optimization algorithms, nor about the settings of their control parameters. The efficiency of the proposed parameter-less algorithm was already evaluated using theoretical and real-world problems, being either real-valued or combinatorial. It was shown that the presented, adaptive, parameter-less algorithm has a faster convergence than comparable algorithms. Furthermore, it demonstrates its search ability by finding the solution without the need for predefined control parameters.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"21 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":"134589085","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: 11

Humanoid learns to detect its own hands 人形机器人学会了检测自己的手

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557729

J. Leitner, Simon Harding, Mikhail Frank, A. Förster, J. Schmidhuber

引用次数: 16

Aerodynamic shape optimization via non-intrusive POD-based surrogate modelling 基于非侵入式pod代理模型的气动外形优化

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557736

E. Iuliano, D. Quagliarella

引用次数: 28