2013 IEEE Congress on Evolutionary Computation最新文献_第10页

A Genetic Programming based approach to automatically generate Wireless Sensor Networks applications 基于遗传规划的无线传感器网络应用程序自动生成方法

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557775

R. R. Oliveira, T. Heimfarth, R. W. Bettio, M. Arantes, C. Toledo

{"title":"A Genetic Programming based approach to automatically generate Wireless Sensor Networks applications","authors":"R. R. Oliveira, T. Heimfarth, R. W. Bettio, M. Arantes, C. Toledo","doi":"10.1109/CEC.2013.6557775","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557775","url":null,"abstract":"The development of Wireless Sensor Networks (WSNs) applications is an arduous task, since the application needs to be customized for each sensor. Thus, the automatic generation of WSN's applications is desirable to reduce costs, since it drastically reduces the human effort. This paper presents the use of Genetic Programming to automatically generate WSNs applications. A scripting language based on events and actions is proposed to represent the WSN behavior. Events represent the state of a given sensor node and actions modify these states. Some events are internal states and others are external states captured by the sensors. The genetic programming is used to automatically generate WSNs applications described using this scripting language. These scripts are executed by all network's sensors. This approach enables the application designer to define only the overall objective of the WSN. This objective is defined by means of a fitness function. An event-detection problem is presented in order to evaluate the proposed method. The results shown the capability of the developed approach to successfully solve WSNs problems through the automatic generation of applications.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"28 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":"131797121","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

Evolutionary medical image registration using automatic parameter tuning 采用自动参数调整的进化医学图像配准

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557718

A. Valsecchi, Jérémie Dubois-Lacoste, T. Stützle, S. Damas, J. Santamaría, L. Marrakchi-Kacem

引用次数: 12

Using good and bad diversity measures in the design of ensemble systems: A genetic algorithm approach 集成系统设计中好坏分集度量的应用:一种遗传算法方法

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557649

Antonino Feitosa Neto, A. Canuto, Teresa B Ludermir

引用次数: 4

Extending features for multilabel classification with swarm biclustering 用群双聚类扩展多标签分类的特征

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557930

R. Prati, F. O. França

引用次数: 9

A new CSP graph-based representation for Ant Colony Optimization 一种新的基于CSP图的蚁群优化表示

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557635

A. González-Pardo, David Camacho

{"title":"A new CSP graph-based representation for Ant Colony Optimization","authors":"A. González-Pardo, David Camacho","doi":"10.1109/CEC.2013.6557635","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557635","url":null,"abstract":"Constraint Satisfaction Problems (CSP) have been widely studied in several research areas like Artificial Intelligence or Operational Research due their complexity and industrial interest. From previous research areas, heuristic (informed) search methods have been particularly active looking for feasible approaches. One of the critical problems to work with CSP is related to the exponential growth of computational resources needed to solve even the simplest problems. This paper presents a new efficient CSP graph-based representation to solve CSP by using Ant Colony Optimization (ACO) algorithms. This paper presents also a new heuristic (called Oblivion Rate), that have been designed to improve the current state-of-the-art in the application of ACO algorithms on these domains. The presented graph construction provides a strong reduction in both, the number of connections and the number of nodes needed to model the CSP. Also, the new heuristic is used to reduce the number of pheromones in the system (allowing to solve problems with an increasing complexity). This new approach has been tested, as case study, using the classical N-Queens Problem. Experimental results show how the new approach works in both, reducing the complexity of the resulting CSP graph and solving problems with increasing complexity through the utilization of the Oblivion Rate.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"5 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":"115551140","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: 19

An Adaptive Velocity Particle Swarm Optimization for high-dimensional function optimization 高维函数优化的自适应速度粒子群算法

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557850

A. A. Martins, A. Adewumi

{"title":"An Adaptive Velocity Particle Swarm Optimization for high-dimensional function optimization","authors":"A. A. Martins, A. Adewumi","doi":"10.1109/CEC.2013.6557850","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557850","url":null,"abstract":"Researchers have achieved varying levels of successes in proposing different methods to modify the particle's velocity updating formula for better performance of Particle Swarm Optimization (PSO). Variants of PSO that solved high-dimensional optimization problems up to 1,000 dimensions without losing superiority to its competitor(s) are rare. Meanwhile, high-dimensional real-world optimization problems are becoming realities hence PSO algorithm therefore needs some reworking to enhance it for better performance in handling such problems. This paper proposes a new PSO variant called Adaptive Velocity PSO (AV-PSO), which adaptively adjusts the velocity of particles based on Euclidean distance between the position of each particle and the position of the global best particle. To avoid getting trapped in local optimal, chaotic characteristics was introduced into the particle position updating formula. In all experiments, it is shown that AV-PSO is very efficient for solving low and high-dimensional global optimization problems. Empirical results show that AV-PSO outperformed AIWPSO, PSOrank, CRIW-PSO, def-PSO, e1-PSO and APSO. It also performed better than LSRS in many of the tested high-dimensional problems. AV-PSO was also used to optimize some high-dimensional problems with 4,000 dimensions with very good results.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"9 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":"117165303","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: 28

A new real-coded genetic algorithm for implicit constrained black-box function optimization 隐式约束黑盒函数优化的实数编码遗传算法

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557920

Kento Uemura, Naotoshi Nakashima, Y. Nagata, I. Ono

{"title":"A new real-coded genetic algorithm for implicit constrained black-box function optimization","authors":"Kento Uemura, Naotoshi Nakashima, Y. Nagata, I. Ono","doi":"10.1109/CEC.2013.6557920","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557920","url":null,"abstract":"In this paper, we propose a new real-coded genetic algorithm (RCGA) for implicit constrained black-box function optimization. On implicit constrained problems, there often exist active constraints of which the optima lie on the boundaries, which makes the problem more difficult. Almost all of conventional constraint-handling techniques cannot be applied to implicit constrained black-box function optimization because we cannot get quantities of constraint violations and preference order of infeasible solutions. The resampling technique may be the only available choice to handle the implicit constraint. AREX/JGG is one of the most powerful RCGAs for non-constrained problems. However, AREX/JGG with resampling technique deteriorates on implicit constrained problems because few individuals are generated near the boundaries of active constraints and, thus, a population cannot approach the boundaries quickly. In order to find these optima, we believe that it is necessary to locate the mode of a distribution for generating new individuals nearer the boundaries. Since solutions around the optima on boundaries of active constraints may have better evaluation values, our proposed method employs the weighted mean of the best half individuals in a population as the mode of the distribution. We assess the proposed method through experiments with some benchmark problems and the results show the proposed method succeeds in finding the optimum with about 40-85% of function evaluations compared to AREX/JGG with resampling technique.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"574 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":"123171917","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

A differential evolution with an orthogonal local search 具有正交局部搜索的差分进化

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557847

Zhenzhen Dai, Aimin Zhou, Guixu Zhang, Sanyi Jiang

引用次数: 17

Overcoming faults using evolution on the PAnDA architecture 利用熊猫架构上的进化来克服错误

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557625

Pedro B. Campos, David M. R. Lawson, S. Bale, James Alfred Walker, M. Trefzer, A. Tyrrell

引用次数: 8

Interactive multi-objective particle swarm optimisation using decision space interaction 基于决策空间交互的交互式多目标粒子群优化

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI: 10.1109/CEC.2013.6557988

Jan Hettenhausen, A. Lewis, M. Randall, T. Kipouros

引用次数: 19