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Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)最新文献

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Measuring evolvability in evolutionary fuzzy robotics 进化模糊机器人的可进化性测量
Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600) Pub Date : 2002-05-12 DOI: 10.1109/CEC.2002.1006227
Seung-Ik Lee, Sung-Bae Cho
{"title":"Measuring evolvability in evolutionary fuzzy robotics","authors":"Seung-Ik Lee, Sung-Bae Cho","doi":"10.1109/CEC.2002.1006227","DOIUrl":"https://doi.org/10.1109/CEC.2002.1006227","url":null,"abstract":"This paper illustrates the evolutionary adaptive process of the rules of a fuzzy controller evolved by a genetic algorithm. Evolutionary activity and schema analysis are used to evaluate and analyze the evolution. The analysis shows that the evolution has been adaptive and final fuzzy rules have evolved from adaptive ones of earlier generations.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121063865","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
Fuzzy controller implementations with fewer than ten transistors? 模糊控制器实现少于十个晶体管?
Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600) Pub Date : 2002-05-12 DOI: 10.1109/CEC.2002.1004521
A. Stoica, R. Zebulum, D. Keymeulen, M. I. Ferguson, V. Duong
{"title":"Fuzzy controller implementations with fewer than ten transistors?","authors":"A. Stoica, R. Zebulum, D. Keymeulen, M. I. Ferguson, V. Duong","doi":"10.1109/CEC.2002.1004521","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004521","url":null,"abstract":"Evolutionary algorithms (EA) offer good promise for automated design of analog circuits as well as for adaptation and automatic reconfiguration of programmable devices. In particular, EAs facilitate the design of analog circuits for very specific requirements, such as those related to the implementation of fuzzy operators, or even of complete fuzzy systems. The paper starts with a brief overview of the evolutionary process applied to circuit design and of a family of analog programmable devices that support on-chip evolution. As a case study, we describe the evolutionary design of a fuzzy controller, using re-configurable analog chips models and unstructured representation. We were able to achieve a circuit that approximates the control surface of a 2-input fuzzy controller, mapping thus a full fuzzy system in only seven transistors. The paper presents evidence that EA can provide very compact solutions for implementation of fuzzy systems, and that programmable analog devices are an efficient and rapid solution for rapid deployment of fuzzy systems.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122503954","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
Learning of neural network parameters using a fuzzy genetic algorithm 使用模糊遗传算法学习神经网络参数
Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600) Pub Date : 2002-05-12 DOI: 10.1109/CEC.2002.1004538
S. Ling, H. Lam, F. Leung, P. Tam
{"title":"Learning of neural network parameters using a fuzzy genetic algorithm","authors":"S. Ling, H. Lam, F. Leung, P. Tam","doi":"10.1109/CEC.2002.1004538","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004538","url":null,"abstract":"This paper presents the learning of neural network parameters using a fuzzy genetic algorithm (GA). The proposed fuzzy GA is modified from the traditional GA with arithmetic crossover and non-uniform mutation. By introducing modified genetic operations, it will be shown that the performance of the proposed fuzzy GA are better than the traditional GA based on some benchmark test functions. Using the fuzzy GA, the parameters of the neural networks can be tuned. An application example on sunspot forecasting is given to show the merits of the proposed fuzzy GA.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122510877","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
GA-hard functions built by combination of Trap functions 由Trap函数组合而成的GA-hard函数
Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600) Pub Date : 2002-05-12 DOI: 10.1109/CEC.2002.1006242
M. Clergue, P. Collard
{"title":"GA-hard functions built by combination of Trap functions","authors":"M. Clergue, P. Collard","doi":"10.1109/CEC.2002.1006242","DOIUrl":"https://doi.org/10.1109/CEC.2002.1006242","url":null,"abstract":"We propose to construct hard functions for genetic algorithms by combining two types of misleading functions. We consider on one hand the traditional Trap functions defined over the unitation, and on the other hand new Trap functions based on the alternation. We recall the performance of GA on these functions as well as the results on the predictive value of the coefficients of correlation between distance to the optimum and fitness. We show that the combination of such functions can generate misleading problems for a genetic algorithm. Moreover, some of these combinations constitute counterexamples for the predictive value of the coefficient of correlation.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121639506","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: 17
On expressiveness of evolutionary computation: is EC algorithmic? 论进化计算的表达性:EC是算法吗?
Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600) Pub Date : 2002-05-12 DOI: 10.1109/CEC.2002.1006988
E. Eberbach
{"title":"On expressiveness of evolutionary computation: is EC algorithmic?","authors":"E. Eberbach","doi":"10.1109/CEC.2002.1006988","DOIUrl":"https://doi.org/10.1109/CEC.2002.1006988","url":null,"abstract":"Evolutionary computation (EC) has traditionally been used for the solution of hard optimization problems. In the general case, solutions found by evolutionary algorithms are satisficing, given current resources and constraints, but not necessarily optimal. Under some conditions, evolutionary algorithms are guaranteed (in infinity) to find an optimal solution. However, evolutionary techniques are not only helpful for dealing with intractable problems. In this paper, we demonstrate, that EC is not restricted to algorithmic methods, and is more expressive than Turing machines.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121792675","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: 16
Real-coded evolutionary algorithms with parent-centric recombination 以母体为中心重组的实数编码进化算法
Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600) Pub Date : 2002-05-12 DOI: 10.1109/CEC.2002.1006210
K. Deb, D. Joshi, Ashish Anand
{"title":"Real-coded evolutionary algorithms with parent-centric recombination","authors":"K. Deb, D. Joshi, Ashish Anand","doi":"10.1109/CEC.2002.1006210","DOIUrl":"https://doi.org/10.1109/CEC.2002.1006210","url":null,"abstract":"Due to an increasing interest in solving real-world optimization problems using evolutionary algorithms (EAs), researchers have developed a number of real-parameter genetic algorithms (GAs) in the recent past. In such studies, the main research effort is spent on developing an efficient recombination operator. Such recombination operators use probability distributions around the parent solutions to create offspring. Some operators emphasize solutions at the center of mass of parents and some around the parents. We propose a generic parent-centric recombination operator (PCX) and compare its performance with a couple of commonly-used mean-centric recombination operators (UNDX and SPX). With the help of a steady-state, elite-preserving, and computationally fast EA model, simulation results show the superiority of PCX on three test problems.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132628150","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: 103
Evolutionary optimized pitching motion control for F-16 aircraft F-16飞机俯仰运动演化优化控制
Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600) Pub Date : 2002-05-12 DOI: 10.1109/CEC.2002.1004540
K. Lee, Chi-Ho Lee, Jong-Hwan Kim, Han-Lim Choi, M. Tahk
{"title":"Evolutionary optimized pitching motion control for F-16 aircraft","authors":"K. Lee, Chi-Ho Lee, Jong-Hwan Kim, Han-Lim Choi, M. Tahk","doi":"10.1109/CEC.2002.1004540","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004540","url":null,"abstract":"A controller to stabilize F-16 aircraft flying with a steady state around the altitude of 25,000 ft is considered. The nonlinear pitching motion model of F-16 is linearized in the range of the flight envelope of velocity vs. altitude. Then the statically unstable system is stabilized by applying feedback linearization. As the gain-scheduling method is introduced at various operating points within the flight envelope, the optimized controller is designed all over the envelope. Evolutionary Optimization based on Lagrangian (Evolian II) is used to optimize the controller gains P and I satisfying complex nonlinear constraints.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132798921","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
Using evolutionary and hybrid algorithms for DC operating point analysis of nonlinear circuits 采用进化算法和混合算法进行非线性电路直流工作点分析
Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600) Pub Date : 2002-05-12 DOI: 10.1109/CEC.2002.1007020
D. Crutchley, Mark Zwolinski
{"title":"Using evolutionary and hybrid algorithms for DC operating point analysis of nonlinear circuits","authors":"D. Crutchley, Mark Zwolinski","doi":"10.1109/CEC.2002.1007020","DOIUrl":"https://doi.org/10.1109/CEC.2002.1007020","url":null,"abstract":"Traditionally, the DC operating points of a nonlinear electronic circuit are found using the Newton-Raphson method, which has known problems. It is not globally convergent; it can frequently diverge; and cannot find multiple solutions in a single pass. We discuss the use of evolutionary algorithms to overcome these problems.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133660959","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
Automated time table generation using multiple context reasoning for university modules 使用大学模块的多个上下文推理自动生成时间表
Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600) Pub Date : 2002-05-12 DOI: 10.1109/CEC.2002.1004507
D. Srinivasan, Tian Hou Seow, Jianxin Xu
{"title":"Automated time table generation using multiple context reasoning for university modules","authors":"D. Srinivasan, Tian Hou Seow, Jianxin Xu","doi":"10.1109/CEC.2002.1004507","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004507","url":null,"abstract":"Finding a feasible lecture/tutorial timetable in a large university department is a challenging problem faced continually in educational establishments. This paper presents an evolutionary algorithm (EA) based approach to solving a heavily constrained university timetabling problem. The approach uses a problem-specific chromosome representation. Heuristics and context-based reasoning have been used for obtaining feasible timetables in a reasonable computing time. An intelligent adaptive mutation scheme has been employed for speeding up the convergence. The comprehensive course timetabling system presented in this paper has been validated, tested and discussed using real world data from a large university.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134159604","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
Step size adaptation in evolution strategies using reinforcement learning 基于强化学习的进化策略中的步长适应
Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600) Pub Date : 2002-05-12 DOI: 10.1109/CEC.2002.1006225
Sibylle D. Müller, N. Schraudolph, P. Koumoutsakos
{"title":"Step size adaptation in evolution strategies using reinforcement learning","authors":"Sibylle D. Müller, N. Schraudolph, P. Koumoutsakos","doi":"10.1109/CEC.2002.1006225","DOIUrl":"https://doi.org/10.1109/CEC.2002.1006225","url":null,"abstract":"We discuss the implementation of a learning algorithm for determining adaptation parameters in evolution strategies. As an initial test case, we consider the application of reinforcement learning for determining the relationship between success rates and the adaptation of step sizes in the (1+1)-evolution strategy. The results from the new adaptive scheme when applied to several test functions are compared with those obtained from the (1+1)-evolution strategy with a priori selected parameters. Our results indicate that assigning good reward measures seems to be crucial to the performance of the combined strategy.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114521236","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: 43
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