Proceedings of the 11th Annual conference on Genetic and evolutionary computation最新文献_第7页

Parallel shared memory strategies for ant-based optimization algorithms 基于蚁群优化算法的并行共享内存策略

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1569903

T. N. Bui, ThanhVu Nguyen, Joseph R. Rizzo

引用次数: 9

A PSO-based framework for dynamic SVM model selection 基于pso的SVM动态模型选择框架

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570066

Marcelo N. Kapp, R. Sabourin, P. Maupin

{"title":"A PSO-based framework for dynamic SVM model selection","authors":"Marcelo N. Kapp, R. Sabourin, P. Maupin","doi":"10.1145/1569901.1570066","DOIUrl":"https://doi.org/10.1145/1569901.1570066","url":null,"abstract":"Support Vector Machines (SVM) are very powerful classifiers in theory but their efficiency in practice rely on an optimal selection of hyper-parameters. A naïve or ad hoc choice of values for the latter can lead to poor performance in terms of generalization error and high complexity of parameterized models obtained in terms of the number of support vectors identified. This hyper-parameter estimation with respect to the aforementioned performance measures is often called the model selection problem in the SVM research community. In this paper we propose a strategy to select optimal SVM models in a dynamic fashion in order to attend that when knowledge about the environment is updated with new observations and previously parameterized models need to be re-evaluated, and in some cases discarded in favour of revised models. This strategy combines the power of the swarm intelligence theory with the conventional grid-search method in order to progressively identify and sort out potential solutions using dynamically updated training datasets. Experimental results demonstrate that the proposed method outperforms the traditional approaches tested against it while saving considerable computational time.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"7 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":"115554398","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: 19

Session details: Track 9: genetic algorithms 议题9:遗传算法

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/3257488

Jano von Hemert, T. Lenaerts

引用次数: 0

Genetic programming for protein related text classification 蛋白质相关文本分类的遗传编程

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570049

M. Segond, C. Fonlupt, D. Robilliard

引用次数: 6

Scalability, generalization and coevolution -- experimental comparisons applied to automated facility layout planning 可扩展性、通用化和协同进化——自动化设施布局规划的实验比较

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1569997

M. Furuholmen, K. Glette, M. Høvin, J. Tørresen

{"title":"Scalability, generalization and coevolution -- experimental comparisons applied to automated facility layout planning","authors":"M. Furuholmen, K. Glette, M. Høvin, J. Tørresen","doi":"10.1145/1569901.1569997","DOIUrl":"https://doi.org/10.1145/1569901.1569997","url":null,"abstract":"Several practical problems in industry are difficult to optimize, both in terms of scalability and representation. Heuristics designed by domain experts are frequently applied to such problems. However, designing optimized heuristics can be a non-trivial task. One such difficult problem is the Facility Layout Problem (FLP) which is concerned with the allocation of activities to space. This paper is concerned with the block layout problem, where the activities require a fixed size and shape (modules). This problem is commonly divided into two sub problems; one of creating an initial feasible layout and one of improving the layout by interchanging the location of activities. We investigate how to extract novel heuristics for the FLP by applying an approach called Cooperative Coevolutionary Gene Expression Programming (CCGEP). By taking advantage of the natural problem decomposition, one species evolves heuristics for pre-scheduling, and another for allocating the activities onto the plant. An experimental, comparative approach investigates various features of the CCGEP approach. The results show that the evolved heuristics converge to suboptimal solutions as the problem size grows. However, coevolution has a positive effect on optimization of single problem instances. Expensive fitness evaluations may be limited by evolving generalized heuristics applicable to unseen fitness cases of arbitrary sizes.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"40 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":"115797390","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

An evolutionary approach to constructive induction for link discovery 链接发现建设性归纳的进化方法

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570248

Tim Weninger, W. Hsu, Jing Xia, Waleed Aljandal

引用次数: 3

An improved secondary ranking for many objective optimization problems 改进了许多目标优化问题的二级排序

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570190

H. Singh, A. Isaacs, T. Ray, W. Smith

引用次数: 2

Facilitating evolutionary innovation by developmental modularity and variability 通过发展模块化和可变性促进进化创新

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1569996

R. Doursat

引用次数: 36

Discovering a domain alphabet 发现域字母表

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570047

Michael D. Schmidt, Hod Lipson

引用次数: 4

Backward time related association rule mining in trafficprediction using genetic network programming withdatabase rearrangement 数据库重排遗传网络规划在交通预测中的后向关联规则挖掘

Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570223

Huiyu Zhou, S. Mabu, K. Shimada, K. Hirasawa

引用次数: 0