2009 IEEE Congress on Evolutionary Computation最新文献_第6页

An analysis of the operation of differential evolution at high and low crossover rates 高交叉率和低交叉率下差分演化操作的分析

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586128

James Montgomery, Stephen Y. Chen

引用次数: 64

Bottom-up evolutionary subspace clustering 自底向上进化子空间聚类

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5585962

Ali Vahdat, M. Heywood, A. N. Zincir-Heywood

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引用次数: 16

Combining multiobjective and single-objective genetic algorithms in heterogeneous island model 结合多目标和单目标遗传算法的异构岛模型

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586075

M. Pilát, Roman Neruda

引用次数: 7

Enhancing digital hardware evolvability with a neuromolecularware design: A biologically-motivated approach 用神经分子软件设计增强数字硬件的可进化性:一种生物学驱动的方法

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586228

Yo-Hsien Lin, Jong-Chen Chen, Wei-Chang Lee, Chung-Chian Hsu

引用次数: 0

Ensemble classifier design by parallel distributed implementation of genetic fuzzy rule selection for large data sets 基于并行分布式实现的大数据集遗传模糊规则选择集成分类器设计

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586393

Y. Nojima, S. Mihara, H. Ishibuchi

{"title":"Ensemble classifier design by parallel distributed implementation of genetic fuzzy rule selection for large data sets","authors":"Y. Nojima, S. Mihara, H. Ishibuchi","doi":"10.1109/CEC.2010.5586393","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586393","url":null,"abstract":"Evolutionary algorithms have been actively applied to knowledge discovery, data mining and machine learning under the name of genetics-based machine learning (GBML). The main advantage of using evolutionary algorithms in those application areas is their flexibility: Various knowledge extraction criteria such as accuracy and complexity can be easily utilized as fitness functions. On the other hand, the main disadvantage is their large computation load. It is not easy to apply evolutionary algorithms to large data sets. The scalability improvement to large data sets is one of the main research issues in GBML. In our former studies, we proposed an idea of parallel distributed implementation of GBML and examined its effectiveness for genetic fuzzy rule selection. The point of our idea was to realize a quadratic speed-up by dividing not only a population but also training data. Training data subsets were periodically rotated over sub-populations in order to prevent each sub-population from over-fitting to a specific training data subset. In this paper, we propose the use of parallel distributed implementation for the design of ensemble classifiers. An ensemble classifier is designed by combining base classifiers, each of which is obtained from each sub-population. Through computational experiments on parallel distributed genetic fuzzy rule selection, we examine the generalization ability of designed ensemble classifiers under various settings with respect to the size of training data subsets and their rotation frequency.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"37 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":"78753056","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: 5

A robust optimization approach using Kriging metamodels for robustness approximation in the CMA-ES 基于Kriging元模型的CMA-ES鲁棒逼近鲁棒优化方法

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586235

J. Kruisselbrink, M. Emmerich, A. Deutz, Thomas Bäck

引用次数: 22

The differential Ant-Stigmergy Algorithm for large-scale global optimization 大规模全局优化的微分反stigmergy算法

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586201

P. Korošec, Katerina Tashkova, J. Silc

引用次数: 21

Bidirectional matrix-based algorithm for 4-qubit reversible logic circuits synthesis 基于双向矩阵的4量子位可逆逻辑电路合成算法

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586417

Dong Wang, Hanwu Chen, Wanning Zhu

引用次数: 4

A progress indicator for detecting success and failure in evolutionary multi-objective optimization 一种用于进化多目标优化成败检测的进度指标

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586352

Luis Martí, Jesús García, A. Berlanga, J. M. Molina

引用次数: 6

A portfolio selection strategy using Genetic Relation Algorithm 基于遗传关系算法的投资组合选择策略

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586430

Yan Chen, S. Mabu, K. Hirasawa

引用次数: 1