2013 IEEE Congress on Evolutionary Computation最新文献_第5页

Hybrid niche Cultural Algorithm for numerical global optimization 数值全局优化的混合生态位文化算法

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557585

Mostafa Z. Ali, Noor H. Awad, R. Reynolds

引用次数: 9

An adaptive penalty function with meta-modeling for constrained problems 约束问题的元建模自适应惩罚函数

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557721

Oliver Kramer, U. Schlachter, Valentin Spreckels

引用次数: 7

Darwinian Robotic Swarms for exploration with minimal communication 达尔文式的机器人群以最少的交流进行探索

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557562

M. Couceiro, R. Rocha, N. Ferreira, P. A. Vargas

引用次数: 11

On the optimality of particle swarm parameters in dynamic environments 动态环境下粒子群参数的最优性研究

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557748

Barend J. Leonard, A. Engelbrecht

引用次数: 18

A genetic-evolutionary model to simulate population dynamics in the Calangos game 用遗传进化模型模拟卡兰戈斯游戏中的种群动态

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557581

V. N. L. Izidoro, L. Castro, Angelo C. Loula

引用次数: 1

A market-based approach to planning in area surveillance 基于市场的区域监测规划方法

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557894

R. L. While, Yick F. Sun, L. Barone

引用次数: 3

An adaptive evolutionary algorithm based on tactical and positional chess problems to adjust the weights of a chess engine 一种基于战术和位置象棋问题的自适应进化算法来调整象棋引擎的权值

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557727

Eduardo Vázquez-Fernández, C. Coello

引用次数: 1

Attribute-based Decision Graphs for multiclass data classification 基于属性的多类数据分类决策图

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557776

J. R. Bertini, M. C. Nicoletti, Liang Zhao

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引用次数: 8

MOMBI: A new metaheuristic for many-objective optimization based on the R2 indicator MOMBI:一种基于R2指标的多目标优化新元启发式算法

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557868

R. Gómez, C. Coello

{"title":"MOMBI: A new metaheuristic for many-objective optimization based on the R2 indicator","authors":"R. Gómez, C. Coello","doi":"10.1109/CEC.2013.6557868","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557868","url":null,"abstract":"The incorporation of performance indicators as the selection mechanism of a multi-objective evolutionary algorithm (MOEA) is a topic that has attracted increasing interest in the last few years. This has been mainly motivated by the fact that Pareto-based selection schemes do not perform properly when solving problems with four or more objectives. The indicator that has been most commonly used for being incorporated in the selection mechanism of a MOEA has been the hypervolume. Here, however, we explore the use of the R2 indicator, which presents some advantages with respect to the hypervolume, the main one being its low computational cost. In this paper, we propose a new MOEA called Many-Objective Metaheuristic Based on the R2 Indicator (MOMBI), which ranks individuals using a utility function. The proposed approach is compared with respect to MOEA/D (based on scalarization) and SMS-EMOA (based on hypervolume) using several benchmark problems. Our preliminary experimental results indicate that MOMBI obtains results of similar quality to those produced by SMS-EMOA, but at a much lower computational cost. Additionally, MOMBI outperforms MOEA/D in most of the test instances adopted, particularly when dealing with high-dimensional problems having complicated Pareto fronts. Thus, we believe that our proposed approach is a viable alternative for solving many-objective optimization problems.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130075615","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: 116

Optimizing visual attention models for predicting human fixations using Genetic Algorithms 利用遗传算法优化预测人类注视的视觉注意模型

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557715

S. Naqvi, Will N. Browne, C. Hollitt

{"title":"Optimizing visual attention models for predicting human fixations using Genetic Algorithms","authors":"S. Naqvi, Will N. Browne, C. Hollitt","doi":"10.1109/CEC.2013.6557715","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557715","url":null,"abstract":"Predicting where humans look in a scene is crucial in tasks like human-computer interaction, design, graphics, image and video compression, and gaze animation. This work proposes the use of a mixed-integer constraint Genetic Algorithm (GA) for searching the optimal parameters of a bio-inspired visual saliency model for accurate prediction of human eye fixations. Bioinspired visual saliency models are complex models, mimicking the primate visual system with a vast choice of design parameters that can be tuned to achieve optimal performance. The bottom-up visual attention model used in this study was trained on three challenging image datasets from the ImgSal database using a standard performance metric (area under Receiver Operating Characteristic curve) as the fitness. To compensate for any bias of the optimized model towards the standard metric, we use two other scoring metrics to assess performance. Performance comparisons with eight state-of-the-art models have been presented for all three scoring metrics. Results show that the proposed GA optimized visual attention model provides better prediction performance than several state-of-the-art models of visual attention.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134437781","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