Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence最新文献_第9页

Dynamic mapping and load balancing with parallel genetic algorithms 基于并行遗传算法的动态映射与负载平衡

Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349946

F. Seredyński

引用次数: 13

Solving small and large scale constraint satisfaction problems using a heuristic-based microgenetic algorithm 基于启发式的微遗传算法求解小型和大型约束满足问题

Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349934

G. Dozier, D. Bahler, J. Bowen

引用次数: 91

An enhanced operator-oriented genetic search algorithm 一种增强的面向算子的遗传搜索算法

Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.350010

Jeffrey D. Stumpf, X. Feng, R. Kelnhofer

引用次数: 7

Scheduling tasks in multiprocessor systems using evolutionary strategies 基于进化策略的多处理器系统任务调度

Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349927

G. Greenwood, Ajay Gupta, Kelly McSweeney

引用次数: 27

Evolution program for resource constrained project scheduling problem 资源受限项目调度问题的演化规划

Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349965

R. Cheng, M. Gen

{"title":"Evolution program for resource constrained project scheduling problem","authors":"R. Cheng, M. Gen","doi":"10.1109/ICEC.1994.349965","DOIUrl":"https://doi.org/10.1109/ICEC.1994.349965","url":null,"abstract":"The paper describes an implementation of an evolution program for a resource constrained project scheduling problem, which is much more complex than other scheduling problems. The traditional order-based crossover operators are not well suited for this problem without modification. The approach adopted is based on the augmentation of the evolution program with domain-specific knowledge. It undertakes the burden of devising appropriate genetic operators for this problem to guarantee a feasible schedule. A new discipline is addressed for designing the genetic operators. In the implementation, crossover is designed to perform blind search to explore the area beyond local optima, and mutation is designed to perform intensive search to produce an improved solution. The proposed approach has been tested on two standard test problems and the results show that it can find the known optimum very rapidly and is superior to existing heuristic techniques. The suggested approach can significantly improve the performance of evolution program both in terms of speed and accuracy and can be applied to other difficult combinatorial optimization problems.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130738511","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: 48

Pattern fed objects: a new approach for textured image segmentation 模式输入对象:一种纹理图像分割的新方法

Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349955

C. Jacquelin, G. Hejblum, A. Aurengo

引用次数: 0

Evolutionary design of FIR digital filters with power-of-two coefficients 2次幂系数FIR数字滤波器的进化设计

Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.350032

Paolo Gentili, F. Piazza, A. Uncini

引用次数: 7

Improving game-tree search with evolutionary neural networks 用进化神经网络改进游戏树搜索

Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349900

David E. Moriarty, R. Miikkulainen

引用次数: 13

Evolutionary solutions to a highly constrained combinatorial problem 高度约束组合问题的进化解

Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349909

R. Piola

引用次数: 14

A comparison study of genetic codings for the traveling salesman problem 旅行商问题遗传编码的比较研究

Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.350052

H. Tamaki, H. Kita, Nobuhiko Shimizu, K. Maekawa, Y. Nishikawa

{"title":"A comparison study of genetic codings for the traveling salesman problem","authors":"H. Tamaki, H. Kita, Nobuhiko Shimizu, K. Maekawa, Y. Nishikawa","doi":"10.1109/ICEC.1994.350052","DOIUrl":"https://doi.org/10.1109/ICEC.1994.350052","url":null,"abstract":"In applying the genetic algorithm (GA) to optimization problems, both a genetic coding method and a method of genetic operations are essential for making a search effective. Moreover, freedom in a genetic representation, e.g. redundant coding, is indispensable for achieving a successful self-organization in GA. This paper treats the case of the application of a GA to the traveling salesman problem (TSP), and proposes four ways of redundantly coding a tour plan. Then, based on several computational experiments, the coding methods have been mutually compared from the viewpoints of the search efficiency, i.e. the effects of genetic operators, the quality of the obtained tours, and the number of generations required for finding near-optimal tours. As a result, the search for the optimal tour is found to be most effective in the case of the coding based on the link information, while the simple GA is found not to be sufficient for solving large-scale problems. Then, the GA is modified by supplementing some new mechanisms. The results of the computational experiments suggest the applicability of the modified GA to large-scale problems.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115537085","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: 30