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Proceedings of the 11th Annual conference on Genetic and evolutionary computation最新文献

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Data-intensive computing for competent genetic algorithms: a pilot study using meandre 胜任遗传算法的数据密集型计算:使用meandre的试点研究
Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570087
Xavier Llorà
{"title":"Data-intensive computing for competent genetic algorithms: a pilot study using meandre","authors":"Xavier Llorà","doi":"10.1145/1569901.1570087","DOIUrl":"https://doi.org/10.1145/1569901.1570087","url":null,"abstract":"Data-intensive computing has positioned itself as a valuable programming paradigm to efficiently approach problems requiring processing very large volumes of data. This paper presents a pilot study about how to apply the data-intensive computing paradigm to evolutionary computation algorithms. Two representative cases (selectorecombinative genetic algorithms and estimation of distribution algorithms) are presented, analyzed, and discussed. This study shows that equivalent data-intensive computing evolutionary computation algorithms can be easily developed, providing robust and scalable algorithms for the multicore-computing era. Experimental results show how such algorithms scale with the number of available cores without further modification.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124197614","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: 13
Genotypic differences and migration policies in an island model 岛屿模型中的基因型差异和移民政策
Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570080
Lourdes Araujo, J. J. M. Guervós, A. García, C. Cotta
{"title":"Genotypic differences and migration policies in an island model","authors":"Lourdes Araujo, J. J. M. Guervós, A. García, C. Cotta","doi":"10.1145/1569901.1570080","DOIUrl":"https://doi.org/10.1145/1569901.1570080","url":null,"abstract":"In this paper we compare different policies to select individuals to migrate in an island model. Our thesis is that choosing individuals in a way that exploits differences between populations can enhance diversity, and improve the system performance. This has lead us to propose a family of policies that we call multikulti, in which nodes exchange individuals different \"enough\" among them. In this paper we present a policy according to which the receiver node chooses the most different individual among the sample received from the sending node. This sample is randomly built but only using individuals with a fitness above a threshold. This threshold is previously established by the receiving node. We have tested our system in two problems previously used in the evaluation of parallel systems, presenting different degree of difficulty. The multikulti policy presented herein has been proved to be more robust than other usual migration policies, such as sending the best or a random individual.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124266713","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: 18
Particle swarm optimization in the presence of multiple global optima 存在多个全局最优的粒子群算法
Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570137
Sunny Choi, B. Mayfield
{"title":"Particle swarm optimization in the presence of multiple global optima","authors":"Sunny Choi, B. Mayfield","doi":"10.1145/1569901.1570137","DOIUrl":"https://doi.org/10.1145/1569901.1570137","url":null,"abstract":"Dynamic analyses of canonical particle swarm optimization (PSO) have indicated that parameter values of phi_max = 4.1 and constriction coefficient chi = 0.729 provide adequate exploration and prevent swarm explosion. This paper shows by example that these values do not prevent swarm explosion in some cases. In other examples it is shown that even when the swarm does not explode, the canonical PSO algorithm with these parameter values can still fail to converge indefinitely. A satisfactory analysis of PSO has yet to be made, and will require abandoning certain assumptions that oversimplify particle behavior.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125594210","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: 1
Free lunches in pareto coevolution 帕累托共同进化中的免费午餐
Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570132
Travis C. Service, D. Tauritz
{"title":"Free lunches in pareto coevolution","authors":"Travis C. Service, D. Tauritz","doi":"10.1145/1569901.1570132","DOIUrl":"https://doi.org/10.1145/1569901.1570132","url":null,"abstract":"Recent work in test based coevolution has focused on employing ideas from multi-objective optimization in coevolutionary domains. So called Pareto coevolution treats the coevolving set of test cases as objectives to be optimized in the sense of multi-objective optimization. Pareto coevolution can be seen as a relaxation of traditional multi-objective evolutionary optimization. Rather than being forced to determine the outcome of a particular individual on every objective, pareto coevolution allows the examination of an individual's outcome on a particular objective. By introducing the notion of certifying pareto dominance and mutual non-dominance, this paper proves for the first time that free lunches exist for the class of pareto coevolutionary optimization problems. This theoretical result is of particular interest because we explicitly provide an algorithm for pareto coevolution which has better performance, on average, than all traditional multi-objective algorithms in the relaxed setting of pareto coevolution. The notion of certificates of preference/non-preference has potential implications for coevolutionary algorithm design in many classes of coevolution as well as for general multi-objective optimization in the relaxed setting of pareto coevolution.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129654325","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
Particle swarm optimization with information share mechanism 基于信息共享机制的粒子群优化
Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570146
Zhi-hui Zhan, Jun Zhang, Rui-zhang Huang
{"title":"Particle swarm optimization with information share mechanism","authors":"Zhi-hui Zhan, Jun Zhang, Rui-zhang Huang","doi":"10.1145/1569901.1570146","DOIUrl":"https://doi.org/10.1145/1569901.1570146","url":null,"abstract":"This paper proposes an information share mechanism into particle swarm optimization (PSO) in order to use all the useful information of the swarm to prevent premature convergence. The particle in traditional PSO uses only the information from its personal best position and the neighborhood's best position. This mechanism is not with sufficient search information and therefore the algorithm is easy to be trapped into local optima. In the proposed information share PSO (ISPSO), all the particles post their best search information to a share device and any particle can read the information on the device and use the information provided by any other particle to help enhance its search ability. Therefore, the ISPSO can use the whole swarm's information to guide the flying direction. The ISPSO has been applied to optimize multimodal functions, and the experimental results demonstrate that the ISPSO can yield better performance when is compared with the traditional and some other improved PSOs.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131294795","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: 0
Particle swarm optimization based multi-prototype ensembles 基于多原型集成的粒子群优化
Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1569910
A. W. Mohemmed, Mark Johnston, Mengjie Zhang
{"title":"Particle swarm optimization based multi-prototype ensembles","authors":"A. W. Mohemmed, Mark Johnston, Mengjie Zhang","doi":"10.1145/1569901.1569910","DOIUrl":"https://doi.org/10.1145/1569901.1569910","url":null,"abstract":"This paper proposes and evaluates a Particle Swarm Optimization (PSO) based ensemble classifier. The members of the ensemble are Nearest Prototype Classifiers generated sequentially using PSO and combined by a majority voting mechanism. Two necessary requirements for good performance of an ensemble are accuracy and diversity of error. Accuracy is achieved by PSO minimizing a fitness function representing the error rate as the members are created. The diversity of error is promoted by using a different initialization of PSO each time to create a new member and by adopting decorrelated training where a penalty term is added to the fitness function to penalize particles that make the same errors as previously generated classifiers. Simulation experiments on different classification problems show that the ensemble has better performance than a single classifier and are effective in generating diverse ensemble members.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131306179","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: 6
Binary encoding for prototype tree of probabilistic model building GP 概率模型构建原型树的二值编码
Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570055
Toshihiko Yanase, Yoshihiko Hasegawa, H. Iba
{"title":"Binary encoding for prototype tree of probabilistic model building GP","authors":"Toshihiko Yanase, Yoshihiko Hasegawa, H. Iba","doi":"10.1145/1569901.1570055","DOIUrl":"https://doi.org/10.1145/1569901.1570055","url":null,"abstract":"In recent years, program evolution algorithms based on the estimation of distribution algorithm (EDA) have been proposed to improve search ability of genetic programming (GP) and to overcome GP-hard problems. One such method is the probabilistic prototype tree (PPT) based algorithm. The PPT based method explores the optimal tree structure by using the full tree whose number of child nodes is maximum among possible trees. This algorithm, however, suffers from problems arising from function nodes having different number of child nodes. These function nodes cause intron nodes, which do not affect the fitness function. Moreover, the function nodes having many child nodes increase the search space and the number of samples necessary for properly constructing the probabilistic model. In order to solve this problem, we propose binary encoding for PPT. Here, we convert each function node to a subtree of binary nodes where the converted tree is correct in grammar. Our method reduces ineffectual search space, and the binary encoded tree is able to express the same tree structures as the original method. The effectiveness of the proposed method is demonstrated through the use of two computational experiments.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116125111","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: 8
Evolved neural fields applied to the stability problem of a simple biped walking model 演化神经场应用于简单两足行走模型的稳定性问题
Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570154
Juan J. Figueredo, Jonatan Gómez
{"title":"Evolved neural fields applied to the stability problem of a simple biped walking model","authors":"Juan J. Figueredo, Jonatan Gómez","doi":"10.1145/1569901.1570154","DOIUrl":"https://doi.org/10.1145/1569901.1570154","url":null,"abstract":"This paper proposes an evolved control architecture based on neural fields for a relatively complex and unstable dynamical system. The neural field model is capable of addressing goal-based planning problems and has properties, like embedding in an Euclidean space and linear stability, that potentially make it well-fitted for dynamic control tasks. The neural field control architecture is tested over the stability problem on a typical inverted-pendulum and the performance of an evolved neural field and a hand-tuned neural field is compared. The neural field controller performs well in the simulation and has a spatial representation which allows interpretation of field potentials. Also, the evolved neural field performs almost as good as the non-evolved one, is more general, and uses a different strategy to control the plant.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127086223","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: 0
Improving prediction in evolutionary algorithms for dynamic environments 改进动态环境下进化算法的预测
Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/1569901.1570021
A. Simoes, E. Costa
{"title":"Improving prediction in evolutionary algorithms for dynamic environments","authors":"A. Simoes, E. Costa","doi":"10.1145/1569901.1570021","DOIUrl":"https://doi.org/10.1145/1569901.1570021","url":null,"abstract":"The addition of prediction mechanisms in Evolutionary Algorithms (EAs) applied to dynamic environments is essential in order to anticipate the changes in the landscape and maximize its adaptability. In previous work, a combination of a linear regression predictor and a Markov chain model was used to enable the EA to estimate when next change will occur and to predict the direction of the change. Knowing when and how the change will occur, the anticipation of the change was made introducing useful information before it happens. In this paper we introduce mechanisms to dynamically adjust the linear predictor in order to achieve higher adaptability and robustness. We also extend previous studies introducing nonlinear change periods in order to evaluate the predictor's accuracy.","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127218756","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: 42
Session details: Track 9: genetic algorithms 议题9:遗传算法
Proceedings of the 11th Annual conference on Genetic and evolutionary computation Pub Date : 2009-07-08 DOI: 10.1145/3257503
Jano von Hemert, T. Lenaerts
{"title":"Session details: Track 9: genetic algorithms","authors":"Jano von Hemert, T. Lenaerts","doi":"10.1145/3257503","DOIUrl":"https://doi.org/10.1145/3257503","url":null,"abstract":"","PeriodicalId":193093,"journal":{"name":"Proceedings of the 11th Annual conference on Genetic and evolutionary computation","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125775302","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: 0
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