2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC)最新文献

筛选
英文 中文
Adaptive genetic programming applied to classification in data mining 自适应遗传规划在数据挖掘分类中的应用
2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC) Pub Date : 2012-11-01 DOI: 10.1109/NaBIC.2012.6402243
Nailah Al-Madi, Simone A. Ludwig
{"title":"Adaptive genetic programming applied to classification in data mining","authors":"Nailah Al-Madi, Simone A. Ludwig","doi":"10.1109/NaBIC.2012.6402243","DOIUrl":"https://doi.org/10.1109/NaBIC.2012.6402243","url":null,"abstract":"Classification is a data mining method that assigns items in a collection to target classes with the goal to accurately predict the target class for each item in the data. Genetic programming (GP) is one of the effective evolutionary computation techniques to solve classification problems, however, it suffers from a long run time. In addition, there are many parameters that need to be set before the GP is run. In this paper, we propose an adaptive GP that automatically determines the best parameters of a run, and executes the classification faster than standard GP. This adaptive GP has three variations. The first variant consists of an adaptive selection process ensuring that the produced solutions in the next generation are better than the solutions in the previous generation. The second variant adapts the crossover and mutation rates by modifying the probabilities ensuring that a solution with a high fitness is protected. And the third variant is an adaptive function list that automatically changes the functions used by deleting the functions that do not favorably contribute to the classification. These proposed variations were implemented and compared to the standard GP. The results show that a significant speedup can be achieved by obtaining similar classification accuracies.","PeriodicalId":103091,"journal":{"name":"2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134505739","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: 8
A building block conservation and extension mechanism for improved performance in Polynomial Symbolic Regression tree-based Genetic Programming 基于多项式符号回归树的遗传规划构建块守恒和扩展机制
2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC) Pub Date : 2012-11-01 DOI: 10.1109/NaBIC.2012.6402250
Anisa W. Ragalo, N. Pillay
{"title":"A building block conservation and extension mechanism for improved performance in Polynomial Symbolic Regression tree-based Genetic Programming","authors":"Anisa W. Ragalo, N. Pillay","doi":"10.1109/NaBIC.2012.6402250","DOIUrl":"https://doi.org/10.1109/NaBIC.2012.6402250","url":null,"abstract":"Polynomial Symbolic Regression tree-based Genetic Programming faces considerable obstacles towards the discovery of a global optimum solution; three of these being bloat, premature convergence and a compromised ability to retain building block information. We present a building block conservation and extension strategy that targets these specific obstacles. Experiments conducted demonstrate a superior performance of our strategy relative to the canonical GP. Further our strategy achieves a competitive reduction in bloat.","PeriodicalId":103091,"journal":{"name":"2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131246085","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: 4
Ant Colony System based approach to single machine scheduling problems: Weighted tardiness scheduling problem 基于蚁群系统的单机调度方法:加权延迟调度问题
2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC) Pub Date : 2012-11-01 DOI: 10.1109/NaBIC.2012.6402244
A. Madureira, D. Falcao, I. Pereira
{"title":"Ant Colony System based approach to single machine scheduling problems: Weighted tardiness scheduling problem","authors":"A. Madureira, D. Falcao, I. Pereira","doi":"10.1109/NaBIC.2012.6402244","DOIUrl":"https://doi.org/10.1109/NaBIC.2012.6402244","url":null,"abstract":"The paper introduces an approach to solve the problem of generating a sequence of jobs that minimizes the total weighted tardiness for a set of jobs to be processed in a single machine. An Ant Colony System based algorithm is validated with benchmark problems available in the OR library. The obtained results were compared with the best available results and were found to be nearer to the optimal. The obtained computational results allowed concluding on their efficiency and effectiveness.","PeriodicalId":103091,"journal":{"name":"2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122511988","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
Neural oscillator for gait command of a humanoid robot 仿人机器人步态控制的神经振荡器
2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC) Pub Date : 2012-11-01 DOI: 10.1109/NaBIC.2012.6402249
R. C. Paiva, A. Romariz, G. Borges
{"title":"Neural oscillator for gait command of a humanoid robot","authors":"R. C. Paiva, A. Romariz, G. Borges","doi":"10.1109/NaBIC.2012.6402249","DOIUrl":"https://doi.org/10.1109/NaBIC.2012.6402249","url":null,"abstract":"In this article bio-inspired techniques are used for generating the gait of a biped robot. The concept of CPG, central pattern generator, which is a neural network capable of producing rhythm output, was used. It was modeled as coupled oscillators. With the purpose of verifying the operation of the oscillators, simulations were made. After that they were implemented for generating the gait for the robot. The output of the oscillators were used as the reference trajectory for the feet and the joint angles were obtained by inverse kinematics. We obtained good result in simulations for the humanoid robot, which showed a degree of tolerance to external perturbations.","PeriodicalId":103091,"journal":{"name":"2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125376883","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
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信