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

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Phenotypic genetic algorithm for partitioning problem 分区问题的表型遗传算法
Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97) Pub Date : 1997-04-13 DOI: 10.1109/ICEC.1997.592372
K. Tagawa, T. Fukui, H. Haneda
{"title":"Phenotypic genetic algorithm for partitioning problem","authors":"K. Tagawa, T. Fukui, H. Haneda","doi":"10.1109/ICEC.1997.592372","DOIUrl":"https://doi.org/10.1109/ICEC.1997.592372","url":null,"abstract":"The paper presents a phenotype based genetic algorithm for solving a partitioning problem, which is partitioning N objects into P groups to optimize an objective function. In the genetic algorithm, a phenotypic individual is represented by a way of division of a suffix set {1,...,N} into P subsets. In order to prevent premature convergence, the paper defines a distance between phenotypic individuals and uses it in the adaptive control of crossover rate. Furthermore, the paper proposes a new crossover operation named weighted edge crossover which preserves both the structure of phenotype and the desirable character of parents. These techniques perform well on a test problem: multiprocessor scheduling problem for robot control computation.","PeriodicalId":167852,"journal":{"name":"Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97)","volume":"12 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":"121705451","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: 4
Asparagos96 and the traveling salesman problem 芦笋和旅行推销员问题
Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97) Pub Date : 1997-04-13 DOI: 10.1109/ICEC.1997.592290
M. Gorges-Schleuter
{"title":"Asparagos96 and the traveling salesman problem","authors":"M. Gorges-Schleuter","doi":"10.1109/ICEC.1997.592290","DOIUrl":"https://doi.org/10.1109/ICEC.1997.592290","url":null,"abstract":"The paper describes a spatially structured evolutionary algorithm being applied to the symmetric and asymmetric traveling salesman problem (TSP). This approach shows that a genetic algorithm with high degree of isolation-by-distance in combination with a simple repairing mechanism is able to find high quality solutions for the TSP. The evolutionary part of the algorithm presented differs from the original version of Asparagos in the choice of the topological pattern being now a ring structure and the support of hierarchy. The application part in contrast has been revised in more depth. A new representation which the author calls the bi-directional array representation is used for the TSP. This representation is invariant concerning the starting point of a tour and allows the realization of a k-Opt move in O(1). The crossover operator MPX is slightly modified in the sense that if there are differing edges in the parent tours, it is now guaranteed that at least one differing edge will occur in the offspring's tour. The mutation operator has been exchanged by the double-bridge 4-Opt move. The complexity of Asparagos96 for the symmetric TSP is O(N/sup 1.1/); for the asymmetric TSP it is O(N).","PeriodicalId":167852,"journal":{"name":"Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97)","volume":"23 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":"127053631","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: 64
Combining modular neural networks developed by evolutionary algorithm 结合进化算法开发的模块化神经网络
Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97) Pub Date : 1997-04-13 DOI: 10.1109/ICEC.1997.592393
Sung-Bae Cho
{"title":"Combining modular neural networks developed by evolutionary algorithm","authors":"Sung-Bae Cho","doi":"10.1109/ICEC.1997.592393","DOIUrl":"https://doi.org/10.1109/ICEC.1997.592393","url":null,"abstract":"The evolutionary approach to artificial neural networks has been developing rapidly in recent years and shows great possibility as a powerful tool. However, most evolutionary neural networks use the simple node as a building block to evolve and select the one network producing the best result after evolution. In this paper, we present concepts and methodologies for evolutionary modular neural networks, which boost the overall performance by combining several potential networks which have emerged during the course of the evolution. Experimental results with the problem of the recognition of handwritten numerals shows the possibility of combining a number of characteristic networks from a gene pool.","PeriodicalId":167852,"journal":{"name":"Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97)","volume":"2 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":"129835580","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
Function approximator design using genetic algorithms 利用遗传算法设计函数逼近器
Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97) Pub Date : 1997-04-13 DOI: 10.1109/ICEC.1997.592365
M. Ahmed, K. DeJong
{"title":"Function approximator design using genetic algorithms","authors":"M. Ahmed, K. DeJong","doi":"10.1109/ICEC.1997.592365","DOIUrl":"https://doi.org/10.1109/ICEC.1997.592365","url":null,"abstract":"The approximation of a mathematical function (using examples in the form of input-output pairs) is a central issue in subjects as diverse as pattern recognition, control theory and statistics. In this paper, we propose an approach for designing a universal function approximator based on a combination of trigonometric and polynomial functions using genetic algorithms (GAs). We performed some experiments using our proposed approach and compared the results to several existing approaches. The results were promising in that the proposed approach was found to be superior to the other approaches in approximating a variety of test functions.","PeriodicalId":167852,"journal":{"name":"Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97)","volume":"1 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":"129208731","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
Genetic programming for vehicle dispatching 车辆调度的遗传规划
Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97) Pub Date : 1997-04-13 DOI: 10.1109/ICEC.1997.592371
I. Benyahia, J. Potvin
{"title":"Genetic programming for vehicle dispatching","authors":"I. Benyahia, J. Potvin","doi":"10.1109/ICEC.1997.592371","DOIUrl":"https://doi.org/10.1109/ICEC.1997.592371","url":null,"abstract":"Vehicle dispatching is aimed at allocating real time service requests to a fleet of vehicles in movement. This task is modeled as a multiattribute choice problem. Namely, different attribute values are associated with each vehicle to describe its situation with respect to the current service request. Based on this attribute description, a utility function that approximates the decision process of a professional dispatcher is computed. This utility function evolves through genetic programming. Computational results are reported on requests collected from a courier service company.","PeriodicalId":167852,"journal":{"name":"Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97)","volume":"27 16 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":"124550381","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
An analysis on linear crossover for real number chromosomes in an infinite population size 无穷大群体中实数染色体的线性交叉分析
Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97) Pub Date : 1997-04-13 DOI: 10.1109/ICEC.1997.592279
T. Nomura
{"title":"An analysis on linear crossover for real number chromosomes in an infinite population size","authors":"T. Nomura","doi":"10.1109/ICEC.1997.592279","DOIUrl":"https://doi.org/10.1109/ICEC.1997.592279","url":null,"abstract":"The article presents an approach for mathematical analysis of genetic algorithms with real number chromosomes. We focus our attention on crossovers using a linear combination of the elements on the locus and verify the property in the change of the distribution of the chromosomes. This framework includes the average crossover and the Unfair Average Crossover we have proposed. We apply this result to these crossover methods.","PeriodicalId":167852,"journal":{"name":"Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97)","volume":"120 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":"117338498","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: 25
Evolutionary segmentation of road traffic scenes 道路交通场景的进化分割
Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97) Pub Date : 1997-04-13 DOI: 10.1109/ICEC.1997.592342
Se Hyun Park, Jong Kook Lee, Hang-Joon Kim
{"title":"Evolutionary segmentation of road traffic scenes","authors":"Se Hyun Park, Jong Kook Lee, Hang-Joon Kim","doi":"10.1109/ICEC.1997.592342","DOIUrl":"https://doi.org/10.1109/ICEC.1997.592342","url":null,"abstract":"Segmenting a car region is an essential stage in the automatic car identification. It is difficult to segment the car region correctly, because road traffic scenes are usually degraded and processing the images is computationally intensive. In this paper, we propose a method of extracting a car region based on color image processing. To segment the color image, we use a distributed genetic algorithm and a Hue-Saturation-Intensity (HSI) color space as a measure of distance. The method offers robustness in dealing with deformation of road scenes and inherent parallelism to improve processing time. A test with road scenes shows an extraction rate of 92.5%. This result suggests that the proposed method works well with real-world situations, and is pertinent to be put into practical use.","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":"133410079","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: 5
Stepping stones and hidden haystacks: when a genetic algorithm defeats a hillclimber 踏脚石和隐藏的干草堆:当遗传算法击败登山者时
Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97) Pub Date : 1997-04-13 DOI: 10.1109/ICEC.1997.592284
D. Corne
{"title":"Stepping stones and hidden haystacks: when a genetic algorithm defeats a hillclimber","authors":"D. Corne","doi":"10.1109/ICEC.1997.592284","DOIUrl":"https://doi.org/10.1109/ICEC.1997.592284","url":null,"abstract":"Following intuitive notions on gross aspects of how a GA behaves, we are able to demonstrate how to construct functions on which a GA will greatly outperform a hillclimber. This augments related work on long path problems, and gene switch cost functions, which describe similarly 'GA appropriate' landscapes but on rather less intuitively clear grounds. Although artificial, the construction of these problems relies on certain gross landscape features that may be a priori estimated in the case of many real problems, incrementing the collection of descriptive tools with which to assess potential amenability to evolutionary search. We argue in particular that a specific notion of hillclimbing behaviour can with certain merits, and with certain qualifications, be included in this collection.","PeriodicalId":167852,"journal":{"name":"Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97)","volume":"220 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":"131544059","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
Stochastic optimization of control parameters in genetic algorithms 遗传算法中控制参数的随机优化
Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97) Pub Date : 1997-04-13 DOI: 10.1109/ICEC.1997.592272
Q.H. Wu, Y.J. Cao
{"title":"Stochastic optimization of control parameters in genetic algorithms","authors":"Q.H. Wu, Y.J. Cao","doi":"10.1109/ICEC.1997.592272","DOIUrl":"https://doi.org/10.1109/ICEC.1997.592272","url":null,"abstract":"The genetic search can be modeled as a controlled Markovian process, the transition of which depends on control parameters (probabilities of crossover and mutation). This paper proposes a stochastic gradient and develops a stochastic approximation algorithm to optimize control parameters of genetic algorithms (GAs). The optimal values of control parameters can be found from a recursive estimation of control parameters provided by the stochastic approximation algorithm. The algorithm performs in finding a stochastic gradient of a given performance index and adapting the control parameters in the direction of descent. Numerical results based on the classical multimodal functions are given to show the effectiveness of the proposed algorithm.","PeriodicalId":167852,"journal":{"name":"Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97)","volume":"57 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":"131686447","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
Joining and rotating data with molecules 用分子连接和旋转数据
Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97) Pub Date : 1997-04-13 DOI: 10.1109/ICEC.1997.592303
Masanori Arita, Masami Hagiya, A. Suyama
{"title":"Joining and rotating data with molecules","authors":"Masanori Arita, Masami Hagiya, A. Suyama","doi":"10.1109/ICEC.1997.592303","DOIUrl":"https://doi.org/10.1109/ICEC.1997.592303","url":null,"abstract":"DNA-based computing is an attempt to solve computational problems with a large number of DNA molecules. Many theoretical results have been reported so far, but their conclusions are seldom supported in experiments. We suggest a data encoding in the form of (tag data tag)+, and report our experimental results of performing concatenation and rotation of DNA. Our results also show the possibility of join and other operations in a relational database with molecules.","PeriodicalId":167852,"journal":{"name":"Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97)","volume":"140 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":"134055535","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: 34
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