2015 IEEE Congress on Evolutionary Computation (CEC)最新文献

A classification and Pareto domination based multiobjective evolutionary algorithm 一种基于分类和Pareto支配的多目标进化算法

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-09-14 DOI: 10.1109/CEC.2015.7257247

Jinyuan Zhang, Aimin Zhou, Guixu Zhang

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引用次数: 58

A minimum population search hybrid for large scale global optimization 一种大规模全局优化的最小种群搜索混合算法

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-09-14 DOI: 10.1109/CEC.2015.7257125

Antonio Bolufé-Röhler, Sonia Fiol-González, Stephen Y. Chen

引用次数: 22

WSN's energy-aware coverage preserving optimization model based on multi-objective bat algorithm 基于多目标蝙蝠算法的WSN能量感知覆盖保持优化模型

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7256927

Marwa Sharawi, E. Emary, I. Saroit, Hesham El-Mahdy

引用次数: 22

Fireworks algorithm for the multi-satellite control resource scheduling problem 烟花算法用于多卫星控制资源调度问题

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257036

Zhenbao Liu, Zuren Feng, Liangjun Ke

引用次数: 28

Balanced civilization map generation based on Open Data 基于开放数据的平衡文明地图生成

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257063

Gabriella A. B. Barros, J. Togelius

引用次数: 14

A GP approach to QoS-aware web service composition including conditional constraints 一种GP方法，用于支持qos的web服务组合，包括条件约束

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257145

Alexandre Sawczuk da Silva, Hui Ma, Mengjie Zhang

{"title":"A GP approach to QoS-aware web service composition including conditional constraints","authors":"Alexandre Sawczuk da Silva, Hui Ma, Mengjie Zhang","doi":"10.1109/CEC.2015.7257145","DOIUrl":"https://doi.org/10.1109/CEC.2015.7257145","url":null,"abstract":"Automated Web service composition is one of the holy grails of service-oriented computing, since it allows users to create an application simply by specifying the inputs the resulting application should require, the outputs it should produce, and any constraints it should respect. The composition problem has been handled using a variety of techniques, from AI planning to optimisation algorithms, however no approach so far has focused on handling three composition dimensions simultaneously, producing solutions that are: (1) fully functional (i.e. fully executable), (2) respect conditional constraints (e.g. user can specify logical branching), and (3) are optimised according to nonfunctional Quality of Service (QoS) measurements. This paper presents a genetic programming approach that addresses these three dimensions simultaneously through the fitness function, as well as through the enforcement of constraints to candidate trees during initialisation, mutation, and crossover. The approach is tested using an extended version of the WSC2008 datasets, and results show that fully functional and quality-optimised solutions can be created for all associated tasks, with an execution time that is roughly equivalent to that of a non-conditional approach.","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116661234","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

Nash reweighting of Monte Carlo simulations: Tsumego 蒙特卡罗模拟的纳什重加权:Tsumego

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257060

D. St-Pierre, Jialin Liu, O. Teytaud

引用次数: 2

A ranking method based on two preference criteria: Chebyshev function and ε-indicator 基于Chebyshev函数和ε-指标两个偏好标准的排序方法

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257240

Antonio López Jaimes, A. Oyama, K. Fujii

引用次数: 9

Parallel particle swarm optimization for reactive power and voltage control verifying dependability 并联粒子群优化无功电压控制可靠性验证

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7256906

Y. Fukuyama

引用次数: 6

Artificial Immune Systems in intelligent agents test 人工免疫系统在智能体中的测试

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7256936

Sávio Mota Carneiro, Thiago Allison Ribeiro da Silva, R. Rabêlo, F. R. V. Silveira, G. Campos

{"title":"Artificial Immune Systems in intelligent agents test","authors":"Sávio Mota Carneiro, Thiago Allison Ribeiro da Silva, R. Rabêlo, F. R. V. Silveira, G. Campos","doi":"10.1109/CEC.2015.7256936","DOIUrl":"https://doi.org/10.1109/CEC.2015.7256936","url":null,"abstract":"Intelligent agents consist in a promising computing technology for the development of complex distributed systems. Despite the available theoretical references for guiding the designer of these agents, there are few proposed testing techniques to validate these systems. It's known that this validation depends on all the selected test cases, which should provide information regarding the components in the structure of the agent that show unsatisfactory performance. This article presents the application of Artificial Immune Systems (AIS), through Clonal Selection Algorithm (CLONALG), for the problem of optimization of selection of test cases for testing computing systems that are based on intelligent agents. In order to validate the use of CLONALG, comparisons between the Genetic Algorithms (GA) and Ant Colony Optimization Algorithms (ACO) techniques were performed. In the experiments with the approach testing intelligent agents with different types of architecture in partially and completely observable environments, the approach selected a group of satisfactory test cases in terms of the generated information about the irregular performance of the agent. From this result, the approach enables the identification of problematic episodes, allowing the designer to make objective changes in the internal structure of the agent in such a way to improve its performance.","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127098991","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