2012 IEEE Congress on Evolutionary Computation最新文献

Simplifying and improving swarm-based clustering 简化和改进基于群的聚类

2012 IEEE Congress on Evolutionary Computation Pub Date : 2012-08-02 DOI: 10.1109/CEC.2012.6252961

Swee Chuan Tan

引用次数: 4

Parallel quantum evolutionary algorithms with Client-Server model for multi-objective optimization on discrete problems 基于客户端-服务器模型的离散问题多目标优化并行量子进化算法

2012 IEEE Congress on Evolutionary Computation Pub Date : 2012-08-02 DOI: 10.1109/CEC.2012.6252958

Wei Xin, S. Fujimura

{"title":"Parallel quantum evolutionary algorithms with Client-Server model for multi-objective optimization on discrete problems","authors":"Wei Xin, S. Fujimura","doi":"10.1109/CEC.2012.6252958","DOIUrl":"https://doi.org/10.1109/CEC.2012.6252958","url":null,"abstract":"This paper proposes a parallel quantum evolutionary algorithm (PQEA) using Client-Server model for multi-objective optimization problems. Firstly, the PQEA uniformly decomposes a multi-objective optimization problem into a number of scalar optimization sub-problems. All the sub-problems are classified into several groups according to their similarities. Each “Client” processes the evolution for a group of neighbor sub-problems in parallel. There is a quantum individual used to address the sub-problems of a group in a “Client”. Since the quantum individual is a probabilistic representation, it can share evolutionary information of the neighbor sub-problems in one group, while the sub-problems are orderly solved using a same q-bit individual. The “Server” maintains non-dominated solutions that are generated by every “Client”. The current best solution for each sub-problem can be found in the “Server”, when the quantum individual updated its states for evolution. Experimental results have demonstrated that PQEA obviously outperforms the most famous multi-objective optimization algorithms MOEA/D on the bi-objectives. For the more objectives, the PQEA obtains the similar results with MOEA/D, even with the same evaluation times. Furthermore, in this paper, the scalability and sensitivity of PQEA have also been experimentally investigated.","PeriodicalId":376837,"journal":{"name":"2012 IEEE Congress on Evolutionary Computation","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115077042","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

Real-time traffic signal control for roundabouts by using a PSO-based fuzzy controller 基于pso模糊控制器的环形交叉路口交通信号实时控制

2012 IEEE Congress on Evolutionary Computation Pub Date : 2012-08-02 DOI: 10.1109/CEC.2012.6256608

Yue-jiao Gong, Jun Zhang

{"title":"Real-time traffic signal control for roundabouts by using a PSO-based fuzzy controller","authors":"Yue-jiao Gong, Jun Zhang","doi":"10.1109/CEC.2012.6256608","DOIUrl":"https://doi.org/10.1109/CEC.2012.6256608","url":null,"abstract":"Developing traffic signal control methods is considered as the most important way to improve the traffic efficiency of modern roundabouts. This paper applies a traffic signal controller with two fuzzy layers for signalizing roundabouts. The outer layer of the controller computes urgency degrees of all the phase subsets and then activates the most urgent subset. This mechanism helps to instantly respond to the current traffic condition of the roundabout so as to improve real-timeness. The inner layer computes extension time of the current phase and decides whether to turn to the next phase in the running phase subset. As the phase sequences are well-designed, the inner layer smoothes the traffic flows which helps to avoid traffic jam. An offline particle swarm optimization (PSO) algorithm is developed to optimize the membership functions adopted in the proposed controller. In this way, the membership functions in the controller are no longer given by human experience, but provided by the intelligent algorithm. Simulation results demonstrate that the proposed controller outperforms previous traffic signal controllers in terms of improving traffic efficiency of modern roundabouts.","PeriodicalId":376837,"journal":{"name":"2012 IEEE Congress on Evolutionary Computation","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114457895","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: 10

Dynamical exploitation space reduction in particle swarm optimization for solving large scale problems 求解大规模问题的粒子群优化动态开发空间缩减

2012 IEEE Congress on Evolutionary Computation Pub Date : 2012-08-02 DOI: 10.1109/CEC.2012.6252937

Shi Cheng, Yuhui Shi, Quande Qin

{"title":"Dynamical exploitation space reduction in particle swarm optimization for solving large scale problems","authors":"Shi Cheng, Yuhui Shi, Quande Qin","doi":"10.1109/CEC.2012.6252937","DOIUrl":"https://doi.org/10.1109/CEC.2012.6252937","url":null,"abstract":"Particle swarm optimization (PSO) may lose search efficiency when the problem's dimension increases to large scale. For high dimensional search space, an algorithm may not be easy to locate at regions which contain good solutions. The exploitation ability is also reduced due to high dimensional search space. The “No Free Lunch” theorem implies that we can make better algorithm if an algorithm knows the information of the problem. Algorithms should have an ability of learning to solve different problems, in other words, algorithms can adaptively change to suit the landscape of problems. In this paper, the strategy of dynamical exploitation space reduction is utilized to learn problems' landscapes. While at the same time, partial re-initialization strategy is utilized to enhance the algorithm's exploration ability. Experimental results show that a PSO with these two strategies has better performance than the standard PSO in large scale problems. Population diversities of variant PSOs, which include position diversity, velocity diversity and cognitive diversity, are discussed and analyzed. From diversity analysis, we can conclude that an algorithm's exploitation ability can be enhanced by exploitation space reduction strategy.","PeriodicalId":376837,"journal":{"name":"2012 IEEE Congress on Evolutionary Computation","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131633757","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: 22

Scaling in distributed evolutionary algorithms with persistent population 具有持久种群的分布式进化算法的缩放

2012 IEEE Congress on Evolutionary Computation Pub Date : 2012-08-02 DOI: 10.1109/CEC.2012.6256622

J. J. M. Guervós, A. García, J. Cruz, Anna I. Esparcia-Alcázar, C. Cotta

引用次数: 9

Interactive genetic algorithm assisted with collective intelligence from group decision making 交互式遗传算法辅助群体决策的集体智慧

2012 IEEE Congress on Evolutionary Computation Pub Date : 2012-08-02 DOI: 10.1109/CEC.2012.6252872

Xiaoyan Sun, Lei Yang, D. Gong, Ming Li

{"title":"Interactive genetic algorithm assisted with collective intelligence from group decision making","authors":"Xiaoyan Sun, Lei Yang, D. Gong, Ming Li","doi":"10.1109/CEC.2012.6252872","DOIUrl":"https://doi.org/10.1109/CEC.2012.6252872","url":null,"abstract":"Interactive genetic algorithms (IGAs) have been successfully applied to optimize problems with aesthetic criteria by embedding the intelligent evaluations of a user into the evolutionary process. User fatigue caused by frequent interactions, however, often greatly impairs the potentials of IGAs on solving complicated optimization problems. Taking the benefits of collective intelligence into account, we here present an IGA with collective intelligence which is derived from a mechanism of group decision making. An IGA with interval individual fitness is focused here and it can be separately conducted by multiple users at the same time. The collective intelligence of all participated users, represented with social and individual knowledge, is first collected by using a modified group decision making method. Then the strategy of applying the collective intelligence to initialize and guide the single evolution of the IGA is given. With such a multi-user promoted IGA framework, the performance of a single IGA is expected to be evidently improved. In a local network environment, the algorithm is applied to a fashion design system and the results empirically demonstrate that the algorithm can not only alleviate user fatigue but also increase the opportunities of IGAs on finding most satisfactory solutions.","PeriodicalId":376837,"journal":{"name":"2012 IEEE Congress on Evolutionary Computation","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130713947","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

Multi-objective optimization using a hybrid differential evolution algorithm 基于混合差分进化算法的多目标优化

2012 IEEE Congress on Evolutionary Computation Pub Date : 2012-06-10 DOI: 10.1109/CEC.2012.6256478

Xianpeng Wang, Lixin Tang

引用次数: 4

A continuous estimation of distribution algorithm by evolving graph structures using reinforcement learning 一种基于强化学习的图结构连续估计分布算法

2012 IEEE Congress on Evolutionary Computation Pub Date : 2012-06-10 DOI: 10.1109/CEC.2012.6256481

Xianneng Li, Bing Li, S. Mabu, K. Hirasawa

{"title":"A continuous estimation of distribution algorithm by evolving graph structures using reinforcement learning","authors":"Xianneng Li, Bing Li, S. Mabu, K. Hirasawa","doi":"10.1109/CEC.2012.6256481","DOIUrl":"https://doi.org/10.1109/CEC.2012.6256481","url":null,"abstract":"A novel graph-based Estimation of Distribution Algorithm (EDA) named Probabilistic Model Building Genetic Network Programming (PMBGNP) has been proposed. Inspired by classical EDAs, PMBGNP memorizes the current best individuals and uses them to estimate a distribution for the generation of the new population. However, PMBGNP can evolve compact programs by representing its solutions as graph structures. Therefore, it can solve a range of problems different from conventional ones in EDA literature, such as data mining and Reinforcement Learning (RL) problems. This paper extends PMBGNP from discrete to continuous search space, which is named PMBGNP-AC. Besides evolving the node connections to determine the optimal graph structures using conventional PMBGNP, Gaussian distribution is used for the distribution of continuous variables of nodes. The mean value μ and standard deviation σ are constructed like those of classical continuous Population-based incremental learning (PBILc). However, a RL technique, i.e., Actor-Critic (AC), is designed to update the parameters (μ and σ). AC allows us to calculate the Temporal-Difference (TD) error to evaluate whether the selection of the continuous value is better or worse than expected. This scalar reinforcement signal can decide whether the tendency to select this continuous value should be strengthened or weakened, allowing us to determine the shape of the probability density functions of the Gaussian distribution. The proposed algorithm is applied to a RL problem, i.e., autonomous robot control, where the robot's wheel speeds and sensor values are continuous. The experimental results show the superiority of PMBGNP-AC comparing with the conventional algorithms.","PeriodicalId":376837,"journal":{"name":"2012 IEEE Congress on Evolutionary Computation","volume":"69 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":"115147892","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

Optimizing the assignment of blood in a blood banking system: Some initial results 优化血库系统中的血液分配:一些初步结果

2012 IEEE Congress on Evolutionary Computation Pub Date : 2012-06-10 DOI: 10.1109/CEC.2012.6256633

A. Adewumi, N. Budlender, M. Olusanya

引用次数: 13

Development of evolutionary data mining algorithms and their applications to cardiac disease diagnosis 进化数据挖掘算法的发展及其在心脏病诊断中的应用

2012 IEEE Congress on Evolutionary Computation Pub Date : 2012-06-10 DOI: 10.1109/CEC.2012.6256640

Jenn-Long Liu, YuHong Hsu, Chih-Lung Hung

引用次数: 19