2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)最新文献_第3页

Study of tumor molecular diagnosis model based on artificial neural network with gene expression profile 基于基因表达谱的人工神经网络肿瘤分子诊断模型研究

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4630926

Xiaogang Ruan, Jinlian Wang, Hui Li, Xiaoming Li

{"title":"Study of tumor molecular diagnosis model based on artificial neural network with gene expression profile","authors":"Xiaogang Ruan, Jinlian Wang, Hui Li, Xiaoming Li","doi":"10.1109/CEC.2008.4630926","DOIUrl":"https://doi.org/10.1109/CEC.2008.4630926","url":null,"abstract":"We introduce a method for modeling cancer diagnosis at the molecular level using a Chinese microarray gastric cancer dataset. The method combines an artificial neural network with a decision tree that is intended to precede standard techniques, such as classification, and enhance their performance and ability to detect cancer genes. First, we used the relief algorithm to select the featured genes that could unravel cancer characteristics out of high dimensional data. Then, an artificial neural network was employed to find the biomarker subsets with the best classification performance for distinguishing cancerous tissues and their counterparts. Next a decision tree expression was used to extract rules subsets from these biomarker sets. Rules induced from the best performance decision tree, in which the branches denote the level of gene expression, were interpreted as a diagnostic model by using previous biological knowledge. Finally, we obtained a gastric cancer diagnosis model for Chinese patients. The results show that using the Chinese gastric biomarker genes with the diagnostic model provides more instruction in biological experiments and clinical diagnosis reference than previous methods.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115546251","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: 1

DPGA: A simple distributed population approach to Taclde uncertainty DPGA:一种简单的分布式种群方法来处理不确定性

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631351

Maumita Bhattacharya

{"title":"DPGA: A simple distributed population approach to Taclde uncertainty","authors":"Maumita Bhattacharya","doi":"10.1109/CEC.2008.4631351","DOIUrl":"https://doi.org/10.1109/CEC.2008.4631351","url":null,"abstract":"Evolutionary algorithms (EA) have been widely accepted as efficient optimizers for complex real life problems. However, many real life optimization problems involve time-variant noisy environment, which pose major challenges to EA-based optimization. Presence of noise interferes with the evaluation and the selection process of EA and adversely affects the performance of the algorithm. Also presence of noise means fitness function can not be evaluated and it has to be estimated instead. Several approaches have been tried to overcome this problem, such as introduction of diversity (hyper mutation, random immigrants, special operators) or incorporation of memory of the past (diploidy, case based memory). In this paper we propose a method, DPGA (distributed population genetic algorithm) that uses a distributed population based architecture to simulate a distributed, self-adaptive memory of the solution space. Local regression is used in each sub-population to estimate the fitness. Specific problem category considered is that of optimization of functions with time variant noisy fitness. Successful applications to benchmark test problems ascertain the proposed methodpsilas superior performance in terms of both adaptability and accuracy.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122334870","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

Evolving scale-free topologies using a Gene Regulatory Network model 进化无标度拓扑使用基因调控网络模型

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631305

Miguel Nicolau, Marc Schoenauer

引用次数: 3

DCA for bot detection DCA用于机器人检测

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631034

Yousof Al-Hammadi, U. Aickelin, Julie Greensmith

{"title":"DCA for bot detection","authors":"Yousof Al-Hammadi, U. Aickelin, Julie Greensmith","doi":"10.1109/CEC.2008.4631034","DOIUrl":"https://doi.org/10.1109/CEC.2008.4631034","url":null,"abstract":"Ensuring the security of computers is a non-trivial task, with many techniques used by malicious users to compromise these systems. In recent years a new threat has emerged in the form of networks of hijacked zombie machines used to perform complex distributed attacks such as denial of service and to obtain sensitive data such as password information. These zombie machines are said to be infected with a dasiahotpsila - a malicious piece of software which is installed on a host machine and is controlled by a remote attacker, termed the dasiabotmaster of a botnetpsila. In this work, we use the biologically inspired dendritic cell algorithm (DCA) to detect the existence of a single hot on a compromised host machine. The DCA is an immune-inspired algorithm based on an abstract model of the behaviour of the dendritic cells of the human body. The basis of anomaly detection performed by the DCA is facilitated using the correlation of behavioural attributes such as keylogging and packet flooding behaviour. The results of the application of the DCA to the detection of a single hot show that the algorithm is a successful technique for the detection of such malicious software without responding to normally running programs.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122020688","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: 68

Evolutionary techniques for precise and real-time implementation of low-power FIR filters 用于精确和实时实现低功耗FIR滤波器的进化技术

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631161

E. F. Stefatos, T. Arslan, A. Hamilton

引用次数: 2

Probabilistic and Graphical Model based Genetic Algorithm Driven Clustering with Instance-level Constraints 基于概率和图模型的遗传算法驱动的实例级约束聚类

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4630817

Yi Hong, S. Kwong, Hanli Wang, Qingsheng Ren, Yuchou Chang

{"title":"Probabilistic and Graphical Model based Genetic Algorithm Driven Clustering with Instance-level Constraints","authors":"Yi Hong, S. Kwong, Hanli Wang, Qingsheng Ren, Yuchou Chang","doi":"10.1109/CEC.2008.4630817","DOIUrl":"https://doi.org/10.1109/CEC.2008.4630817","url":null,"abstract":"Clustering is traditionally viewed as an unsupervised method for data analysis. However, several recent studies have shown that some limited prior instance-level knowledge can significantly improve the performance of clustering algorithm. This paper proposes a semi-supervised clustering algorithm termed as the probabilistic and graphical model based genetic algorithm driven clustering with instance-level constraints (Cop-CGA). In Cop-CGA, all prior knowledge about pairs of instances that should or should not be classified into the same groups is denoted as a graph and all candidate clustering solutions are sampled from this graph with different orders to assign instances into a certain number of groups. We illustrate how to design the Cop-CGA to guarantee that all candidate solutions satisfy the given constraints and demonstrate the usefulness of background knowledge for genetic algorithm driven clustering algorithm through experiments on several real data sets with artificial hard constraints. One advantage of Cop-CGA is both positive and negative instance-level constraints can be easily incorporated. Moreover, the performance of Cop-CGA is not sensitive to the order of assignment of instances to groups.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"14 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129703641","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: 9

On-orbit long-range maneuver transfer via EDAs 通过EDAs进行在轨远程机动转移

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631110

Laihong Hu, F. Sun, Hualong Xu, Huaping Liu, Fengge Wu

引用次数: 4

Fuzzy constraint-directed negotiation mechanism as a framework for multi-agent scheduling 模糊约束导向协商机制作为多智能体调度的框架

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631218

K. R. Lai, Bo-Ruei Kao, Yi-Yuan Chiang

引用次数: 1

Evolving in silico bistable and oscillatory dynamics for gene regulatory network motifs 基因调控网络基序的硅双稳态和振荡动力学演化

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4630826

Yaochu Jin, B. Sendhoff

引用次数: 22

On the generality of the evolved driving rules of an agent operating a model of a car 驾驶汽车模型的智能体演化驾驶规则的通用性

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631362

Hirotaka Yamazaki, I. Tanev, T. Hiroyasu, K. Shimohara

{"title":"On the generality of the evolved driving rules of an agent operating a model of a car","authors":"Hirotaka Yamazaki, I. Tanev, T. Hiroyasu, K. Shimohara","doi":"10.1109/CEC.2008.4631362","DOIUrl":"https://doi.org/10.1109/CEC.2008.4631362","url":null,"abstract":"We present an approach for automated evolutionary design of the functionary of driving agent, able to operate a software model of fast running car. The objective of our work is to automatically discover a set of driving rules (if existent) that are general enough to be able to adequately control the car in all sections of predefined circuits. In order to evolve an agent with such capabilities, we propose an indirect, generative representation of the driving rules as algebraic functions of the features of the current surroundings of the car. These functions, when evaluated for the current surrounding of the car yield concrete values of the main attributes of the driving style (e.g., straight line velocity, turning velocity, etc.), applied by the agent in the currently negotiated section of the circuit. Experimental results verify both the very existence of the general driving rules and the ability of the employed genetic programming framework to automatically discover them. The evolved driving rules offer a favorable generality, in that a single rule can be successfully applied (i) not only for all the section of a particular circuit, but also (ii) for the sections in several a priori defined circuits featuring different characteristics.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130524558","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: 0