2012 IEEE Congress on Evolutionary Computation最新文献_第9页

Community evolution in a scientific collaboration network 科学合作网络中的社区进化

2012 IEEE Congress on Evolutionary Computation Pub Date : 2012-06-10 DOI: 10.1109/CEC.2012.6256434

M. V. Nguyen, M. Kirley, R. García-Flores

引用次数: 10

Optimizing risk management using NSGA-II 利用NSGA-II优化风险管理

2012 IEEE Congress on Evolutionary Computation Pub Date : 2012-06-10 DOI: 10.1109/CEC.2012.6256509

Marcos Álvares Barbosa Junior, Fernando Buarque de Lima-Neto, T. Marwala

{"title":"Optimizing risk management using NSGA-II","authors":"Marcos Álvares Barbosa Junior, Fernando Buarque de Lima-Neto, T. Marwala","doi":"10.1109/CEC.2012.6256509","DOIUrl":"https://doi.org/10.1109/CEC.2012.6256509","url":null,"abstract":"Companies are often susceptible to uncertainties which can disturb the achievement of their objectives. The effect of these uncertainties can be perceived as risk that will be taken. A healthful company have to anticipate undesired events by defining a process for managing risks. Risk management processes are responsible for identifying, analyzing and evaluating risky scenarios and whether they should undergo control in order to satisfy a previously defined risk criteria. Risk specialists have to consider, at the same time, many operational aspects (decision variables) and objectives to decide which and when risk treatments have to be executed. In line with that, most companies select risks to be treated by using expertise of human specialists or simple sorting heuristics based on the believed impact. Companies have limited resources (e.g. human and financial resources) and risk treatments have costs which the selection process has to deal with. Aiming to balancing the competition between risk and resource management this paper proposes a new optimization step within the standard risk management methodology created by the International Organization for Standardization (a.k.a. ISO). To test the resulted methodology, experiments based on the Non-dominated Sorting Genetic Algorithm (more specifically NSGA-II) were performed aiming to manage risk and resources of a simulated company. Results show us that the proposed approach can deal with multiple conflicting objectives reducing the risk exposure time by selecting risks to be treated according their impact and available resources.","PeriodicalId":376837,"journal":{"name":"2012 IEEE Congress on Evolutionary Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122206801","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

A preliminary study of a new multi-objective optimization algorithm 一种新的多目标优化算法的初步研究

2012 IEEE Congress on Evolutionary Computation Pub Date : 2012-06-10 DOI: 10.1109/CEC.2012.6256437

V. Lattarulo, G. Parks

引用次数: 11

Evolving multi-objective strategies for task allocation of scientific workflows on public clouds 公共云上科学工作流任务分配的演进多目标策略

2012 IEEE Congress on Evolutionary Computation Pub Date : 2012-06-10 DOI: 10.1109/CEC.2012.6256556

Claudia Szabo, Trent Kroeger

{"title":"Evolving multi-objective strategies for task allocation of scientific workflows on public clouds","authors":"Claudia Szabo, Trent Kroeger","doi":"10.1109/CEC.2012.6256556","DOIUrl":"https://doi.org/10.1109/CEC.2012.6256556","url":null,"abstract":"With the increase in deployment of scientific application on public and private clouds, the allocation of workflow tasks to specific cloud instances to reduce runtime and cost has emerged as an important challenge. The allocation of scientific workflows on public clouds can be described through a variety of perspectives and parameters and has been proved to be NP-complete. This paper presents an optimization framework for task allocation on public clouds. We present a solution that considers important parameters such as workflow runtime, communication overhead, and overall execution cost. Our multi-objective optimization framework builds on a simple and extensible cost model and uses a heuristic to determine the optimal number of cloud instances to be used. Using the Amazon Elastic Compute Cloud (EC2) and Amazon Simple Storage Service (S3) as an example, we show how our optimization heuristics lead to significantly better strategies than other state-of-the-art approaches. Specifically, our single-objective optimization is slightly better than a simple heuristic and a particle swarm optimization approach for small workflows, and achieves significant improvements for larger workflows. In a similar manner, our multi-objective optimization obtains similar results to our single-objective optimization for small-size workflows, and achieves up to 80% improvement for large-size workflows.","PeriodicalId":376837,"journal":{"name":"2012 IEEE Congress on Evolutionary Computation","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126079372","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: 47

Describing Quantum-Inspired Linear Genetic Programming from symbolic regression problems 从符号回归问题描述量子启发的线性遗传规划

2012 IEEE Congress on Evolutionary Computation Pub Date : 2012-06-10 DOI: 10.1109/CEC.2012.6256634

D. Dias, M. Pacheco

引用次数: 2

Hybrid ACO and TOFA feature selection approach for text classification 混合ACO和TOFA特征选择方法的文本分类

2012 IEEE Congress on Evolutionary Computation Pub Date : 2012-06-10 DOI: 10.1109/CEC.2012.6252960

H. Alghamdi, Lilian Tang, S. Alshomrani

{"title":"Hybrid ACO and TOFA feature selection approach for text classification","authors":"H. Alghamdi, Lilian Tang, S. Alshomrani","doi":"10.1109/CEC.2012.6252960","DOIUrl":"https://doi.org/10.1109/CEC.2012.6252960","url":null,"abstract":"With the highly increasing availability of text data on the Internet, the process of selecting an appropriate set of features for text classification becomes more important, for not only reducing the dimensionality of the feature space, but also for improving the classification performance. This paper proposes a novel feature selection approach to improve the performance of text classifier based on an integration of Ant Colony Optimization algorithm (ACO) and Trace Oriented Feature Analysis (TOFA). ACO is metaheuristic search algorithm derived by the study of foraging behavior of real ants, specifically the pheromone communication to find the shortest path to the food source. TOFA is a unified optimization framework developed to integrate and unify several state-of-the-art dimension reduction algorithms through optimization framework. It has been shown in previous research that ACO is one of the promising approaches for optimization and feature selection problems. TOFA is capable of dealing with large scale text data and can be applied to several text analysis applications such as text classification, clustering and retrieval. For classification performance yet effective, the proposed approach makes use of TOFA and classifier performance as heuristic information of ACO. The results on Reuters and Brown public datasets demonstrate the effectiveness of the proposed approach.","PeriodicalId":376837,"journal":{"name":"2012 IEEE Congress on Evolutionary Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124991108","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: 11

Adaptive Quantum-inspired Evolution Strategy 自适应量子进化策略

2012 IEEE Congress on Evolutionary Computation Pub Date : 2012-06-10 DOI: 10.1109/CEC.2012.6256433

Hamid Izadinia, M. Ebadzadeh

引用次数: 2

A memory binary particle swarm optimization

2012 IEEE Congress on Evolutionary Computation Pub Date : 2012-06-10 DOI: 10.1109/CEC.2012.6256150

Z. Ji, Tao Tian, Shan He, Zexuan Zhu

引用次数: 5

An Adaptive Memetic Algorithm using a synergy of Differential Evolution and Learning Automata 基于差分进化和学习自动机的自适应模因算法

2012 IEEE Congress on Evolutionary Computation Pub Date : 2012-06-10 DOI: 10.1109/CEC.2012.6256574

Abhronil Sengupta, T. Chakraborti, A. Konar, Eunjin Kim, A. Nagar

引用次数: 10

Equality constrained long-short portfolio replication by using probabilistic model-building GA 基于概率建模遗传算法的等式约束多空投资组合复制

2012 IEEE Congress on Evolutionary Computation Pub Date : 2012-06-10 DOI: 10.1109/CEC.2012.6256174

Y. Orito, Hisashi Yamamoto, Y. Tsujimura

{"title":"Equality constrained long-short portfolio replication by using probabilistic model-building GA","authors":"Y. Orito, Hisashi Yamamoto, Y. Tsujimura","doi":"10.1109/CEC.2012.6256174","DOIUrl":"https://doi.org/10.1109/CEC.2012.6256174","url":null,"abstract":"Portfolio replication problem is to optimize the portfolio such that its proportion-weighted combination is the same as the given benchmark portfolio. However, the benchmark portfolio generally opens only the return to the public but other information such as the assets included in the portfolio, the proportion-weighted combination, the rebalancing date and the investment strategies is closed to the public. In order to optimize such portfolios, we propose an optimization method based on the probabilistic model-building GA in this paper. On the other hand, we are focusing on the long-short portfolio optimization. The long-short portfolio consists of the assets with long positions in which they have bought and been held and with short positions in which they have been borrowed and sold. While applying any optimization method to the long-short portfolios, the portfolio as a feasible solution must be satisfied an equality constraint. In order to make the feasible solutions effectively, we propose two techniques and then apply them to our optimization method. In the numerical experiments, we show that our method has better ability to replicate the long-short portfolios with good fitness values. We found that, however, some portfolios were not replicated though our method worked well. We also discuss this problem in this paper.","PeriodicalId":376837,"journal":{"name":"2012 IEEE Congress on Evolutionary Computation","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131612754","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: 12