Proceedings of the 11th Annual conference on Genetic and evolutionary computation最新文献_第10页

A multiobjective evolutionary algorithm for the task based sailor assignment problem 基于任务的水手分配问题的多目标进化算法

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570099

D. Dasgupta, Fernando Niño, D. Garrett, Koyel Chaudhuri, Soujanya Medapati, Aishwarya Kaushal, James Simien

引用次数: 8

A mixed discrete-continuous attribute list representation for large scale classification domains 大规模分类域的混合离散-连续属性表表示

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570057

J. Bacardit, N. Krasnogor

{"title":"A mixed discrete-continuous attribute list representation for large scale classification domains","authors":"J. Bacardit, N. Krasnogor","doi":"10.1145/1569901.1570057","DOIUrl":"https://doi.org/10.1145/1569901.1570057","url":null,"abstract":"Datasets with a large number of attributes are a difficult challenge for evolutionary learning techniques. The recently proposed attribute list rule representation has shown to be able to significantly improve the overall performance (e.g. run-time, accuracy, rule set size) of the BioHEL Iterative Evolutionary Rule Learning system. In this paper we, first, extend the attribute list rule representation so it can handle not only continuous domains, but also datasets with a very large number of mixed discrete-continuous attributes. Secondly, we benchmark the new representation with a diverse set of large-scale datasets and, third, we compare the new algorithms with several well-known machine learning methods. The experimental results we describe in the paper show that the new representation is equal or better than the state of-the-art in evolutionary rule representations both in terms of the accuracy obtained with the benchmark datasets used, as well as in terms of the computational time requirements needed to achieve these improved accuracies. The new attribute list representation puts BioHEL on an equal footing with other well-established machine learning techniques in terms of accuracy. In the paper, we also analyse and discuss the current weaknesses behind the current representation and indicate potential avenues for correcting them.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128455506","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: 30

On the hybridization of SMS-EMOA and local search for continuous multiobjective optimization SMS-EMOA与局部搜索的杂交研究

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1569985

P. Koch, Oliver Kramer, G. Rudolph, N. Beume

引用次数: 16

Genetic programming based image segmentation with applications to biomedical object detection 基于遗传规划的图像分割及其在生物医学目标检测中的应用

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570052

T. Singh, N. Kharma, M. Daoud, R. Ward

引用次数: 23

Creating regular expressions as mRNA motifs with GP to predict human exon splitting 用GP创建正则表达式作为mRNA基序来预测人类外显子分裂

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570162

W. Langdon, Joanna Rowsell, A. Harrison

{"title":"Creating regular expressions as mRNA motifs with GP to predict human exon splitting","authors":"W. Langdon, Joanna Rowsell, A. Harrison","doi":"10.1145/1569901.1570162","DOIUrl":"https://doi.org/10.1145/1569901.1570162","url":null,"abstract":"RNAnet [3] http://bioinformatics.essex.ac.uk/users/wlangdon/rnanet/ allows the user to calculate correlations of gene expression, both between genes and between components within genes. We investigate all of Ensembl http://www.ensembl.org and find all the Homo Sapiens exons for which there are sufficient robust Affymetrix HG-U133 Plus 2 GeneChip probes. Calculating correlation between mRNA probe measurements for the same exon shows many exons whose components are consistently up regulated and down regulated. However we identify other Ensembl exons where sub-regions within them are self consistent but these transcript blocks are not well correlated with other blocks in the same exon. We suggest many current Ensembl exon definitions are incomplete. Secondly, having identified exon with substructure we use machine learning to try and identify patterns in the DNA sequence lying between blocks of high correlation which might yield biological or technological explanations. A Backus-Naur form (BNF) context-free grammar constrains strongly typed genetic programming (STGP) to evolve biological motifs in the form of regular expressions (RE) (e.g. TCTTT) which classify gene exons with potential alternative mRNA expression from those without. We show biological patterns can be data mined by a GP written in gawk and using egrep from NCBI's GEO http://www.ncbi.nlm.nih.gov/geo/ database. The automatically produced DNA motifs suggest that alternative polyadenylation is not responsible. (Full version in TR-09-02 [7].) Blocky exons can be found in http://bioinformatics.essex.ac.uk/users/wlangdon/tr-09-02.tar.gz","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115672671","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: 7

The effect of vesicular selection in dynamic environments 动态环境中囊泡选择的影响

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570207

Yun-Geun Lee, R. McKay, N. X. Hoai, Dong-Kyun Kim

引用次数: 2

Evolutionary algorithms and multi-objectivization for the travelling salesman problem 旅行商问题的进化算法与多目标化

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1569984

Martin Jähne, Xiaodong Li, J. Branke

引用次数: 42

Session details: Track 7: evolutionary multiobjective optimization 分会报告7:进化多目标优化

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/3257486

D. Corne, Joshua D. Knowles

引用次数: 0

An adaptive strategy for improving the performance of genetic programming-based approaches to evolutionary testing 一种改进基于遗传规划的进化测试方法性能的自适应策略

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570253

J. Ribeiro, M. Z. Rela, F. F. Vega

引用次数: 0

A hybrid GA-PSO fuzzy system for user identification on smart phones 一种混合GA-PSO模糊智能手机用户识别系统

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570117

Muhammad Shahzad, Saira Zahid, M. Farooq

{"title":"A hybrid GA-PSO fuzzy system for user identification on smart phones","authors":"Muhammad Shahzad, Saira Zahid, M. Farooq","doi":"10.1145/1569901.1570117","DOIUrl":"https://doi.org/10.1145/1569901.1570117","url":null,"abstract":"The major contribution of this paper is a hybrid GA-PSO fuzzy user identification system, UGuard, for smart phones. Our system gets 3 phone usage features as input to identify a user or an imposter. We show that these phone usage features for different users are diffused; therefore, we justify the need of a front end fuzzy classifier for them. We further show that the fuzzy classifier must be optimized using a back end online dynamic optimizer. The dynamic optimizer is a hybrid of Particle Swarm Optimizer (PSO) and Genetic Algorithm (GA). We have collected phone usage data of 10 real users having Symbian smart phones for 8 days. We evaluate our UGuard system on this dataset. The results of our experiments show that UGuard provides on the average an error rate of 2% or less. We also compared our system with four classical classifiers -- Na¨1ve Bayes, Back Propagation Neural Networks, J48 Decision Tree, and Fuzzy System -- and three evolutionary schemes -- fuzzy system optimized by ACO, PSO, and GA. To the best of our knowledge, the current work is the first system that has achieved such a small error rate. Moreover, the system is simple and efficient; therefore, it can be deployed on real world smart phones.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"435 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126106015","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: 18