2009 IEEE Congress on Evolutionary Computation最新文献_第7页

Distributed genetic process mining 分布式遗传过程挖掘

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586250

Carmen Bratosin, N. Sidorova, Wil M.P. van der Aalst

引用次数: 34

Evolving efficient limit order strategy using Grammatical Evolution 用语法进化进化有效的限位顺序策略

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586040

Wei Cui, A. Brabazon, M. O’Neill

引用次数: 6

Constrained optimization by the ε constrained differential evolution with an archive and gradient-based mutation 基于存档和梯度突变的ε约束差分进化约束优化

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586484

T. Takahama, S. Sakai

{"title":"Constrained optimization by the ε constrained differential evolution with an archive and gradient-based mutation","authors":"T. Takahama, S. Sakai","doi":"10.1109/CEC.2010.5586484","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586484","url":null,"abstract":"The ε constrained method is an algorithm transformation method, which can convert algorithms for unconstrained problems to algorithms for constrained problems using the ε level comparison, which compares search points based on the pair of objective value and constraint violation of them. We have proposed the ε constrained differential evolution (εDE), which is the combination of the ε constrained method and differential evolution (DE). It has been shown that the εDE can run very fast and can find very high quality solutions. Also, we proposed the εDE with gradient-based mutation (εDEg), which utilized gradients of constraints in order to solve problems with difficult constraints. In this study, we propose the ε constrained DE with an archive and gradient-based mutation (εDEag). The εDEag utilizes an archive to maintain the diversity of individuals and adopts a new way of selecting the ε level control parameter in the εDEg. The 18 problems, which are given in special session on “Single Objective Constrained RealParameter Optimization” in CEC2010, are solved by the εDEag and the results are shown in this paper.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"108 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81574610","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: 267

Functionalization of microarray devices: Process optimization using a multiobjective PSO and multiresponse MARS modeling 微阵列器件的功能化:使用多目标粒子群和多响应MARS建模的过程优化

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586165

Laura Villanova, P. Falcaro, D. Carta, I. Poli, Rob J Hyndman, K. Smith‐Miles

{"title":"Functionalization of microarray devices: Process optimization using a multiobjective PSO and multiresponse MARS modeling","authors":"Laura Villanova, P. Falcaro, D. Carta, I. Poli, Rob J Hyndman, K. Smith‐Miles","doi":"10.1109/CEC.2010.5586165","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586165","url":null,"abstract":"An evolutionary approach for the optimization of microarray coatings produced via sol-gel chemistry is presented. The aim of the methodology is to face the challenging aspects of the problem: unknown objective function, high dimensional variable space, constraints on the independent variables, multiple responses, expensive or time-consuming experimental trials, expected complexity of the functional relationships between independent and response variables. The proposed approach iteratively selects a set of experiments by combining a multiob-jective Particle Swarm Optimization (PSO) and a multiresponse Multivariate Adaptive Regression Splines (MARS) model. At each iteration of the algorithm the selected experiments are implemented and evaluated, and the system response is used as a feedback for the selection of the new trials. The performance of the approach is measured in terms of improvements with respect to the best coating obtained changing one variable at a time (the method typically used by scientists). Relevant enhancements have been detected, and the proposed evolutionary approach is shown to be a useful methodology for process optimization with great promise for industrial applications.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"11 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81820574","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: 3

Optimising multi-modal polynomial mutation operators for multi-objective problem classes 多目标问题的优化综合多项式变异算子类

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586076

Kent McClymont, E. Keedwell

引用次数: 1

Unveiling Skype encrypted tunnels using GP 揭开Skype加密隧道使用GP

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586288

Riyad Alshammari, A. N. Zincir-Heywood

引用次数: 34

Cooperative Co-evolution for large scale optimization through more frequent random grouping 通过更频繁的随机分组进行大规模优化的协同进化

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586127

M. Omidvar, Xiaodong Li, Zhenyu Yang, X. Yao

{"title":"Cooperative Co-evolution for large scale optimization through more frequent random grouping","authors":"M. Omidvar, Xiaodong Li, Zhenyu Yang, X. Yao","doi":"10.1109/CEC.2010.5586127","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586127","url":null,"abstract":"In this paper we propose three techniques to improve the performance of one of the major algorithms for large scale continuous global function optimization. Multilevel Cooperative Co-evolution (MLCC) is based on a Cooperative Co-evolutionary framework and employs a technique called random grouping in order to group interacting variables in one subcomponent. It also uses another technique called adaptive weighting for co-adaptation of subcomponents. We prove that the probability of grouping interacting variables in one subcomponent using random grouping drops significantly as the number of interacting variables increases. This calls for more frequent random grouping of variables. We show how to increase the frequency of random grouping without increasing the number of fitness evaluations. We also show that adaptive weighting is ineffective and in most cases fails to improve the quality of found solution, and hence wastes considerable amount of CPU time by extra evaluations of objective function. Finally we propose a new technique for self-adaptation of the subcomponent sizes in CC. We demonstrate how a substantial improvement can be gained by applying these three techniques.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"24 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85227524","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: 208

Evolutionary design of reversible digital circuits using IMEP the case of the even parity problem 基于IMEP的可逆数字电路的进化设计——偶宇称问题

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586252

F. Hadjam, C. Moraga

引用次数: 4

A genetic hyperheuristic algorithm for the resource constrained project scheduling problem 资源约束下项目调度问题的遗传超启发式算法

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586488

K. Anagnostopoulos, G. Koulinas

引用次数: 16

A comparative analysis of genetic algorithm and ant colony optimization to select attributes for an heterogeneous ensemble of classifiers 遗传算法与蚁群算法在异构分类器集合属性选择中的比较分析

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586080

L. E. A. Santana, Ligia Silva, A. Canuto, F. Pintro, K. Vale

引用次数: 51