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Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97)最新文献

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Tuning of a fuzzy logic power system stabilizer using genetic algorithms 基于遗传算法的模糊逻辑电力系统稳定器整定
Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97) Pub Date : 1997-04-13 DOI: 10.1109/ICEC.1997.592380
M. A. Abido, Y. Abdel-Magid
{"title":"Tuning of a fuzzy logic power system stabilizer using genetic algorithms","authors":"M. A. Abido, Y. Abdel-Magid","doi":"10.1109/ICEC.1997.592380","DOIUrl":"https://doi.org/10.1109/ICEC.1997.592380","url":null,"abstract":"A Hybrid Power System Stabilizer (HPSS) is presented. The proposed approach uses genetic algorithms (GA) to search for optimal or near optimal settings of fuzzy logic power system stabilizer (FLPSS) parameters. Incorporation of GA in FLPSSs design will add an intelligent dimension to these stabilizers and significantly reduce the time consumed in the design process. It is shown that the performance of FLPSS can be improved significantly by incorporating a genetic based learning mechanism. The performance of the proposed HPSS under different disturbances and loading conditions is investigated. The results show the superiority and robustness of the proposed HPSS as compared to classical PSS and its capability to enhance system damping over a wide range of loading conditions.","PeriodicalId":167852,"journal":{"name":"Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97)","volume":"264 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123290712","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
Evolutionary search guided by the constraint network to solve CSP 约束网络引导下的进化搜索求解CSP
Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97) Pub Date : 1997-04-13 DOI: 10.1109/ICEC.1997.592332
M. Riff-Rojas
{"title":"Evolutionary search guided by the constraint network to solve CSP","authors":"M. Riff-Rojas","doi":"10.1109/ICEC.1997.592332","DOIUrl":"https://doi.org/10.1109/ICEC.1997.592332","url":null,"abstract":"We are interested in defining a general evolutionary algorithm to solve constraint satisfaction problems, which takes into account both advantages of the systematic and traditional methods and of characteristics of the CSP. In this context knowledge about properties of the constraint network has allowed us to define a fitness function, for evaluation (Riff, 1996). We introduce two new operators which look at the constraint network during evolution. The first one is a bisexual operator like crossover denominated arc-crossover, for exploitation. The second one is an operator like mutation called arc-mutation, for exploration. These operators are used to improve the stochastic search.","PeriodicalId":167852,"journal":{"name":"Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116503446","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: 32
Evolutionary computation applied to 3D image reconstruction 进化计算在三维图像重建中的应用
Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97) Pub Date : 1997-04-13 DOI: 10.1109/ICEC.1997.592356
Y. Fujiwara, H. Sawai
{"title":"Evolutionary computation applied to 3D image reconstruction","authors":"Y. Fujiwara, H. Sawai","doi":"10.1109/ICEC.1997.592356","DOIUrl":"https://doi.org/10.1109/ICEC.1997.592356","url":null,"abstract":"We apply a simple algorithm for evolutionary computation to the problem of approximating a human facial 3D image by a triangular mesh under the condition that the number of data points is restricted. The essential point of this problem is to locate the data points in such a way that the resulting triangular mesh approximates the facial surface as well as possible. Selection and reproduction of the data points, based on the approximation error, are shown to be effective for solving this problem even in its simplest algorithmic implementation. We also argue that such evolutionary computation might be useful in other engineering fields.","PeriodicalId":167852,"journal":{"name":"Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124891494","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
The mixing evolutionary algorithm-independent selection and allocation of trials 不依赖进化算法的混合试验选择与分配
Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97) Pub Date : 1997-04-13 DOI: 10.1109/ICEC.1997.592260
C. van Kemenade
{"title":"The mixing evolutionary algorithm-independent selection and allocation of trials","authors":"C. van Kemenade","doi":"10.1109/ICEC.1997.592260","DOIUrl":"https://doi.org/10.1109/ICEC.1997.592260","url":null,"abstract":"When using an evolutionary algorithm to solve a problem involving building blocks, we have to grow the building blocks and then mix these building blocks to obtain the (optimal) solution. Finding a good balance between the growing and the mixing process is a prerequisite to get a reliable evolutionary algorithm. Different building blocks can have different probabilities of being mixed. Such differences can easily lead to a loss of the building blocks that are difficult to mix and as a result to premature convergence. By allocating a relatively large amount of trials to individuals that contain building blocks with a low mixing probability, we can prevent such effects. We developed the mixing evolutionary algorithm (mixEA) in which the allocation of trials is a more explicit procedure than in the standard evolutionary algorithms. Experiments indicate that the mixEA is a reliable optimizer on a set of building block problems that are difficult to handle with more traditional genetic algorithms. In the case that the global optimum is not found, the mixEA creates a small population containing a high concentration of building blocks.","PeriodicalId":167852,"journal":{"name":"Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121774402","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
Evolving modular neural networks which generalise well 进化的模块化神经网络泛化良好
Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97) Pub Date : 1997-04-13 DOI: 10.1109/ICEC.1997.592382
Yong Liu, X. Yao
{"title":"Evolving modular neural networks which generalise well","authors":"Yong Liu, X. Yao","doi":"10.1109/ICEC.1997.592382","DOIUrl":"https://doi.org/10.1109/ICEC.1997.592382","url":null,"abstract":"In dealing with complex problems, a monolithic neural network often becomes too large and complex to design and manage. The only practical way is to design modular neural network systems consisting of simple modules. While there has been a lot of work on combining different modules in a modular system in the fields of neural networks, statistics and machine learning, little work has been done on how to design those modules automatically and how to exploit the interaction between individual module design and module combination. This paper proposes an evolutionary approach to designing modular neural networks. The approach addresses the issue of automatic determination of the number of individual modules and the exploitation of the interaction between individual module design and module combination. The relationship among different modules is considered during the module design. This is quite different from the conventional approach where the module design is separated from the module combination. Experimental results on some benchmark problems are presented and discussed in this paper.","PeriodicalId":167852,"journal":{"name":"Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122546225","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: 35
Applying genetic programming to evolve behavior primitives and arbitrators for mobile robots 应用遗传规划进化移动机器人的行为原语和仲裁器
Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97) Pub Date : 1997-04-13 DOI: 10.1109/ICEC.1997.592362
Wei-Po Lee, John Hallam, Henrik Hautop Lund
{"title":"Applying genetic programming to evolve behavior primitives and arbitrators for mobile robots","authors":"Wei-Po Lee, John Hallam, Henrik Hautop Lund","doi":"10.1109/ICEC.1997.592362","DOIUrl":"https://doi.org/10.1109/ICEC.1997.592362","url":null,"abstract":"The behavior-based approach has been successfully applied to designing robot control systems. This paper presents our work, based on evolutionary algorithms, to program behavior-based robots automatically. Instead of hand-coding all the behavior controllers or evolving an entire control system for an overall task, we suggest our approach at the intermediate level: it includes evolving behavior primitives and behavior arbitrators for a mobile robot to achieve the specified tasks. To examine the developed approach, we evolve a control system for a moderately complicated box-pushing task as an example. We first evolved the controllers in a simulation and then transferred them to the Khepera miniature robot. Experimental results show the promise of our approach, and the evolved controllers are transferred to the real robot without loss of performance.","PeriodicalId":167852,"journal":{"name":"Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121209628","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: 86
Solving optimal control problems with a cost changing control by evolutionary algorithms 用进化算法求解成本变化控制的最优控制问题
Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97) Pub Date : 1997-04-13 DOI: 10.1109/ICEC.1997.592331
J. Bobbin, X. Yao
{"title":"Solving optimal control problems with a cost changing control by evolutionary algorithms","authors":"J. Bobbin, X. Yao","doi":"10.1109/ICEC.1997.592331","DOIUrl":"https://doi.org/10.1109/ICEC.1997.592331","url":null,"abstract":"Many mathematical solutions to certain classes of optimal control problems, particularly problems which give rise to 'chattering controls', make some physically unrealistic assumptions in order to solve the problems. These solutions often ignore the cost of changing control and thus fail to give physically realistic results due to the physical reality of this cost in many applications. When this cost is incorporated into the problem, the problem can become very difficult to solve numerically. The paper considers an evolutionary approach to solving optimal control problems which take the cost of changing control into account. A novel chromosome representation and an insert mutation have been proposed and tested against three different problems. The experimental results show that the evolutionary approach is quite competitive in comparison with the existing method based on dynamic programming.","PeriodicalId":167852,"journal":{"name":"Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117101567","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
Splicing systems and molecular processes 剪接系统和分子过程
Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97) Pub Date : 1997-04-13 DOI: 10.1109/ICEC.1997.592296
T. Head
{"title":"Splicing systems and molecular processes","authors":"T. Head","doi":"10.1109/ICEC.1997.592296","DOIUrl":"https://doi.org/10.1109/ICEC.1997.592296","url":null,"abstract":"The splicing system concept and its history are reviewed. A proposed laboratory splicing scheme is discussed. This scheme has suggested that splicing schemes be regarded as specifying not only languages, but also dynamical systems. As an example of a new formal result on splicing languages, a theorem is stated that characterizes those regular languages that are generated by splicing systems which require only one-sided context. The theorem provides an algorithm for deciding whether any arbitrary regular language can be so generated.","PeriodicalId":167852,"journal":{"name":"Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116925152","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
A genetic algorithm for the minimum weight triangulation 最小权值三角剖分的遗传算法
Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97) Pub Date : 1997-04-13 DOI: 10.1109/ICEC.1997.592370
K. Qin, Wenping Wang, Minglun Gong
{"title":"A genetic algorithm for the minimum weight triangulation","authors":"K. Qin, Wenping Wang, Minglun Gong","doi":"10.1109/ICEC.1997.592370","DOIUrl":"https://doi.org/10.1109/ICEC.1997.592370","url":null,"abstract":"In this paper, a new method for the minimum weight triangulation of points on a plane, called genetic minimum weight triangulation (GMWT), is presented based on the rationale of genetic algorithms. Polygon crossover and its algorithm for triangulations are proposed. New adaptive genetic operators, or adaptive crossover and mutation operators, are introduced. It is shown that the new method for the minimum weight triangulation can obtain more optimal results of triangulations than the greedy algorithm.","PeriodicalId":167852,"journal":{"name":"Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122270321","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
Fast feature selection with genetic algorithms: a filter approach 基于遗传算法的快速特征选择:一种滤波方法
Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97) Pub Date : 1997-04-13 DOI: 10.1109/ICEC.1997.592369
P. Lanzi
{"title":"Fast feature selection with genetic algorithms: a filter approach","authors":"P. Lanzi","doi":"10.1109/ICEC.1997.592369","DOIUrl":"https://doi.org/10.1109/ICEC.1997.592369","url":null,"abstract":"The goal of the feature selection process is, given a dataset described by n attributes (features), to find the minimum number m of relevant attributes which describe the data as well as the original set of attributes do. Genetic algorithms have been used to implement feature selection algorithms. Previous algorithms presented in the literature used the predictive accuracy of a specific learning algorithm as the fitness function to maximize over the space of possible feature subsets. Such an approach to feature selection requires a large amount of CPU time to reach a good solution on large datasets. This paper presents a genetic algorithm for feature selection which improves previous results presented in the literature for genetic-based feature selection. It is independent of a specific learning algorithm and requires less CPU time to reach a relevant subset of features. Reported experiments show that the proposed algorithm is at least ten times faster than a standard genetic algorithm for feature selection without a loss of predictive accuracy when a learning algorithm is applied to reduced data.","PeriodicalId":167852,"journal":{"name":"Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128500924","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: 119
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