2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)最新文献_第6页

A nonlinear program model to obtain consensus priority vector in the analytic hierarchy process 在层次分析法中建立了一种非线性规划模型，以获得共识优先向量

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4630884

Wei-jun Xu, Yucheng Dong, Weilin Xiao, Jinhong Xu

引用次数: 1

A dialectical approach for classification of DW-MR Alzheimer’s images DW-MR阿尔茨海默病图像的辨证分类方法

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631023

W. Santos, R. E. D. Souza, Plínio B. Santos Filho, Fernando Buarque de Lima-Neto, F. D. Assis

引用次数: 16

A non-revisiting simulated annealing algorithm 一种非重访模拟退火算法

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631046

S. Y. Yuen, C. Chow

引用次数: 13

Optimizing melting rate and fuel consumption of rotary furnace using NSGA -II 利用NSGA -II优化回转炉的熔化速率和燃料消耗

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631310

K. K. Mishra, B. Singh, Akash Punhani, L. Sharma

引用次数: 3

A modified Trigonometric Differential Evolution algorithm for optimization of dynamic systems 一种用于动态系统优化的改进三角微分进化算法

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4630986

Rakesh Angira, A. Santosh

{"title":"A modified Trigonometric Differential Evolution algorithm for optimization of dynamic systems","authors":"Rakesh Angira, A. Santosh","doi":"10.1109/CEC.2008.4630986","DOIUrl":"https://doi.org/10.1109/CEC.2008.4630986","url":null,"abstract":"Differential evolution (DE) is a novel evolutionary algorithm capable of handling non-differentiable, nonlinear and multimodal objective functions. Previous studies have shown that DE is an efficient, effective and robust evolutionary optimization method. Still it takes large computational time for solving the computationally expensive objective functions (for example optimization problems in the areas of computational mechanics, computational fluid dynamics, optimal control etc.) And therefore, an attempt to speed up DE is considered necessary. This paper deals with application and evaluation of a modified version of trigonometric differential evolution (TDE) algorithm. The modification in TDE algorithm is made to further enhance its convergence speed. Further the modified trigonometric differential evolution (MTDE) algorithm is applied and evaluated for solving dynamic optimization problems encountered in chemical engineering. The performance of MTDE algorithm is compared with that of TDE and original DE algorithms. Results indicate that the MTDE algorithm is efficient and significantly faster than TDE and DE algorithms.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"13 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":"131819141","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: 7

The social fabric approach as an approach to knowledge integration in Cultural Algorithms 文化算法中知识整合的社会结构方法

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631371

R. Reynolds, Mostafa Z. Ali

引用次数: 10

Adaptive GA: An essential ingredient in high-level synthesis 自适应遗传算法:高水平合成的重要组成部分

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631319

Florence Chiao Choong Mei, S. Phon-Amnuaisuk, M. Y. Alias, P. W. Leong

{"title":"Adaptive GA: An essential ingredient in high-level synthesis","authors":"Florence Chiao Choong Mei, S. Phon-Amnuaisuk, M. Y. Alias, P. W. Leong","doi":"10.1109/CEC.2008.4631319","DOIUrl":"https://doi.org/10.1109/CEC.2008.4631319","url":null,"abstract":"High-level synthesis, a crucial step in VLSI and system on chip (SoC) design, is the process of transforming an algorithmic or behavioral description into a structural specification of the architecture realizing the behavior. In the past, researchers have attempted to apply GAs to the HLS domain. This is motivated by the fact that the search space for HLS is large and GAs are known to work well on such problems. However, the process of GA is controlled by several parameters, e.g. crossover rate and mutation rate that largely determine the success and efficiency of GA in solving a specific problem. Unfortunately, these parameters interact with each other in a complicated way and determining which parameter set is best to use for a specific problem can be a complex task requiring much trial and error. This inherent drawback is overcome in this paper where it presents two adaptive GA approaches to HLS, the adaptive GA operator probability (AGAOP) and adaptive operator selection (AOS) and compares the performance to the standard GA (SGA) on eight digital logic benchmarks with varying complexity. The AGAOP and AOS are shown to be far more robust than the SGA, providing fast and reliable convergence across a broad range of parameter settings. The results show considerable promise for adaptive approaches to HLS domain and opens up a path for future work in this area.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"4 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":"132643487","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: 3

Exploring population structures for locally concurrent and massively parallel Evolutionary Algorithms 探索局部并发和大规模并行进化算法的种群结构

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631148

J. L. Laredo, Pedro Ángel Castillo Valdivieso, A. García, J. J. M. Guervós

{"title":"Exploring population structures for locally concurrent and massively parallel Evolutionary Algorithms","authors":"J. L. Laredo, Pedro Ángel Castillo Valdivieso, A. García, J. J. M. Guervós","doi":"10.1109/CEC.2008.4631148","DOIUrl":"https://doi.org/10.1109/CEC.2008.4631148","url":null,"abstract":"In this paper we present the Gossip-based Evolvable Agent Model (GossEvAg) within the context of parallel fine-grained Evolutionary Algorithms (EAs). It extends the Cellular Evolutionary Algorithm (CEA) definition with two novel features designed to work on Peer-to-Peer (P2P) networks: every individual is self-scheduled in a single thread and dynamically self-organizes its neighbourhood via newscasting, a gossip protocol. As a consequence of such multi-threading model, each Evolvable Agent (EvAg) updates asynchronously its state at random depending on the underlying platform scheduler. In order to assess the effects of asynchrony and the gossip protocol, we perform an experimental evaluation of the model for a set of discrete optimization problems. As a baseline for comparison we use two canonical genetic algorithms (GA): A steady-state GA (ssGA) and a generational GA (gGA). We also test two more topologies for the EvAg, a complete graph topology which allows panmixia and a Watts-Strogatz topology which has shown good theoretical and empirical results in related papers. We found that leaving the management of the EvAg to the underlying platform scheduler has an interesting emerging feature: the model is able to scale seamlessly in desktop computers without any effort from the practitioner. We measure how the algorithm speed scales by conducting the experiments in a Single and a Dual-Core Processor architectures.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"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":"131169026","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: 23

Genetic algorithm for the vehicle routing problem with time windows and fuzzy demand 具有时间窗和模糊需求的车辆路径问题的遗传算法

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631360

Jian Xu, G. Goncalves, T. Hsu

引用次数: 9

Ant-based query processing for replicated events in wireless sensor networks 无线传感器网络中基于蚁群的复制事件查询处理

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4630789

Jianping Yu, Yaping Lin, Jinhua Zheng

{"title":"Ant-based query processing for replicated events in wireless sensor networks","authors":"Jianping Yu, Yaping Lin, Jinhua Zheng","doi":"10.1109/CEC.2008.4630789","DOIUrl":"https://doi.org/10.1109/CEC.2008.4630789","url":null,"abstract":"Wireless sensor networks are often deployed in diverse application specific contexts and one unifying view is to treat them essentially as distributed databases. The simplest mechanism to obtain information from this kind of database is to flood queries for named data within the network and obtain the relevant responses from sources. However, if the queries are issued for replicated data, the simple approach can be highly inefficient. As sensor networks are uniquely characterized by limited energy availability and low memory, alternative strategies need to be examined for this kind of queries. A novel query processing approach using distributed Multiple Ant Colonies algorithm with positive interaction is presented in this paper, in which ants adjust individual behavior via cooperation to make colony behavior intelligent, demanding merely local information to find named data efficiently and determine the number and allocation of event replicas adaptively. Theoretically and experimentally, the results clearly show that the proposed protocol is more flexible and energy-efficient than existing algorithms.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"37 6 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":"132849909","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