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Proceedings of 1995 IEEE International Conference on Evolutionary Computation最新文献

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Hierarchical description of two dimensional shapes using a genetic algorithm 利用遗传算法对二维形状进行分层描述
Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-12-01 DOI: 10.1109/ICEC.1995.487458
Peisuei Lee, T. Nagao
{"title":"Hierarchical description of two dimensional shapes using a genetic algorithm","authors":"Peisuei Lee, T. Nagao","doi":"10.1109/ICEC.1995.487458","DOIUrl":"https://doi.org/10.1109/ICEC.1995.487458","url":null,"abstract":"A description method for arbitrary two dimensional shapes is proposed in this paper. When a 2D shape is given as a silhouette, its structure is automatically approximated by the use of a set of rectangles by the proposed method. Sizes, positions and rotational angles of the rectangles which approximate adequately the given 2D shape are searched by a genetic algorithm; GA. In our coding of GA, a chromosome of each individual is a bit string corresponding to parameter sets of several rectangles. Through a generation iteration, accuracy of approximation of the given 2D shape is improved. The total number of rectangles to be used for description is assumed to be given before shape description. By changing the total number of rectangles, hierarchical description of given 2D shapes is achieved. This method can be applied to shape description and object recognition in the field of computer vision and to abstraction of 2D shapes in the field of artistic applications by the use of computers.","PeriodicalId":213919,"journal":{"name":"Proceedings of 1995 IEEE International Conference on Evolutionary Computation","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134089807","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
An improved genetic algorithm for large scale distribution systems loss minimum problem 大型配电系统损耗最小问题的改进遗传算法
Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.489128
K. Nara
{"title":"An improved genetic algorithm for large scale distribution systems loss minimum problem","authors":"K. Nara","doi":"10.1109/ICEC.1995.489128","DOIUrl":"https://doi.org/10.1109/ICEC.1995.489128","url":null,"abstract":"In this paper, an improved application of the genetic algorithm to a large scale distribution systems planning is discussed. The genetic algorithm can find a sub -optimal solution of a large scale combinatorial optimization problem, by simulating the adoptive nature of natural genetics, faster than the simulated annealing method and more accurate than the existing heuristic approximate solution algorithm. However, from the author's experi ences in applying the algorithm ta the distribution loss minimization problem, calculation results are accurate enough for small scale systems, but are not so accurate for large scale systems. The reason of this may be that so-called \"implicit parallelism\" does not work well for large scale problems because of the huge number of combination of solutions. Therefore, this paper discusses improvements of the simple genetic algorithm needed to refine the accuracy for large scale power systems planning problems. The following operators and improvements are tested and discussed in this paper. (1)Crossover between strings with high fitness function values, (2)Number of crossover points, (3)Mutation by burst of bits, (4)Reduction of infeasible strings, (5)Selection of initial strings, (6)Change of distribution of fitness function values. The performances of these improvements are compared with each other through comparative numerical results.","PeriodicalId":213919,"journal":{"name":"Proceedings of 1995 IEEE International Conference on Evolutionary Computation","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125224995","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
On the application of genetic programming to chemical process systems 遗传规划在化工过程系统中的应用
Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.487470
B. McKay, M. Willis, G. Barton
{"title":"On the application of genetic programming to chemical process systems","authors":"B. McKay, M. Willis, G. Barton","doi":"10.1109/ICEC.1995.487470","DOIUrl":"https://doi.org/10.1109/ICEC.1995.487470","url":null,"abstract":"A genetic programming approach is utilised to develop mathematical models of chemical process systems. Having discussed genetic programming in general, two examples are used to reveal the utility of the technique. It is shown how the method can discriminate between relevant and irrelevant process inputs, evolving to yield parsimonious model structures that accurately represent process characteristics. This removes the need for restrictive assumptions about the form of the data and the structure of the required model. In addition, as the technique determines complex nonlinear relationships in the data, non-intuitive process features are revealed with comparative ease.","PeriodicalId":213919,"journal":{"name":"Proceedings of 1995 IEEE International Conference on Evolutionary Computation","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116686719","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
Integration of constraint solving techniques in genetic algorithms 遗传算法中约束求解技术的集成
Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.489115
R. Bruns
{"title":"Integration of constraint solving techniques in genetic algorithms","authors":"R. Bruns","doi":"10.1109/ICEC.1995.489115","DOIUrl":"https://doi.org/10.1109/ICEC.1995.489115","url":null,"abstract":"A new method for constraint handling in genetic algorithms is presented in this paper. It provides a way of representing and handling constraints that is both general and problem-independent. The main idea behind this approach lies in the augmentation of genetic search with constrizint solving techniques. Thus providing a general means for the handling of any kind of constraints and the incorporation of problem-specific knowledge in genetic algorithms. The application of the method is illustrated by a job shop scheduling problem. The results indicate that the method is ,competitive to previous problem-specific GA-based approaches when applied to scheduling problems.","PeriodicalId":213919,"journal":{"name":"Proceedings of 1995 IEEE International Conference on Evolutionary Computation","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116755367","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
Combination of direct global and local optimization methods 直接全局和局部优化方法的结合
Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.489168
M. Syrjakov, H. Szczerbicka
{"title":"Combination of direct global and local optimization methods","authors":"M. Syrjakov, H. Szczerbicka","doi":"10.1109/ICEC.1995.489168","DOIUrl":"https://doi.org/10.1109/ICEC.1995.489168","url":null,"abstract":"Today, a great shortcoming of the existing direct global optimization methods like Genetic Algorithms, Evolution Strategies, Simulated Annealing, etc. is, that they are only approximation algorithms usually requiring high numbers of cost function evaluations. Hence, in case of cost functions which are expensive to evaluate, these algorithms are not applicable any more. In this paper, some powerful direct parameter optimization algorithms are presented, being combinations of direct global and local search methods. Beyond that, the basic structure of an optimization strategy is described, which is able to accomplish an extensive analysis of the optimum points of a given cost function (multiple-stage optimization). Our developed methods are implemented and integrated into REM0 (REsearch Model Optimization package) 19, 111 representing a software tool for experimentation and optimization of simulation models. Some optimization results are presented to demonstrate that our approach successfully focuses the advantages of global and local search.","PeriodicalId":213919,"journal":{"name":"Proceedings of 1995 IEEE International Conference on Evolutionary Computation","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124929107","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: 11
An evolutionary and cooperative agents model for optimization 面向优化的进化合作智能体模型
Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.487464
F. Abbattista, N. Abbattista, L. Caponetti
{"title":"An evolutionary and cooperative agents model for optimization","authors":"F. Abbattista, N. Abbattista, L. Caponetti","doi":"10.1109/ICEC.1995.487464","DOIUrl":"https://doi.org/10.1109/ICEC.1995.487464","url":null,"abstract":"The authors propose the use of genetic algorithms (GA) to optimize another algorithm for optimization. The aim is to integrate the approach introduced by Dorigo et al., known as the ant system, with GA, exploiting the cooperative effect of the latter and the evolutionary effect of GA. An ant algorithm aims to solve problems of combinatorial optimization by means of a population of agents/processors that work parallel without a supervisor in a cooperative manner. A genetic algorithm aims to optimize the performance of the ant population by selecting optimal values for its parameters by means of evolution of the genetic patrimony associated with each single agent. The approach has been applied to the traveling salesman problem; results and comparisons with the original method are presented.","PeriodicalId":213919,"journal":{"name":"Proceedings of 1995 IEEE International Conference on Evolutionary Computation","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125161322","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
Power flow control in FACTS using evolutionary programming 基于进化规划的FACTS功率流控制
Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.489126
L. Lai, J.T. Ma
{"title":"Power flow control in FACTS using evolutionary programming","authors":"L. Lai, J.T. Ma","doi":"10.1109/ICEC.1995.489126","DOIUrl":"https://doi.org/10.1109/ICEC.1995.489126","url":null,"abstract":"This paper presents the use of an evolutionary programming (EP) to solve optimal power flow (OPF) problems in flexible AC transmission systems (FACTS). The unified power flow controller (UPFC) is used as a phase shifter and/or series compensator to regulate both angles and magnitude of branch voltages. EP, coupled with PQ power flow, selects the best regulation to minimize the real power loss and keep the power flows in their secure limits.","PeriodicalId":213919,"journal":{"name":"Proceedings of 1995 IEEE International Conference on Evolutionary Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114133940","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: 27
Genetic logic programming 遗传逻辑程序设计
Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.487475
T. Osborn, Adib Charif, Ricardo Lamas, E. Dubossarsky
{"title":"Genetic logic programming","authors":"T. Osborn, Adib Charif, Ricardo Lamas, E. Dubossarsky","doi":"10.1109/ICEC.1995.487475","DOIUrl":"https://doi.org/10.1109/ICEC.1995.487475","url":null,"abstract":"Genetic logic programming (GLP) is a new method which applies the genetic algorithm paradigm to declarative programming-specifically to evolve populations of Prolog programs. This paper examines GLP applied to natural language understanding to illustrate the power, issues and limitations of GLP. Populations of Prolog query interpreters evolve to respond more correctly to queries about Aesop's fable \"The Fox and the Crow\". The interpreters process parsed text and consult a general knowledge-base. The gene pool consists of a large set of Prolog rules and facts which are tentatively proposed as being 'useful' for interpretation. Essentially, interpreters act as an interface between queries, knowledge bases and the text. Closure and termination are addressed at the level of design of the gene pool, and various Prolog options. Fitness amounts to a score on a high-school-like \"comprehension test\", with special care needed to deal with redundant and dependent answers, and with an eye to rewarding correct higher-level abstractions.","PeriodicalId":213919,"journal":{"name":"Proceedings of 1995 IEEE International Conference on Evolutionary Computation","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128970393","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
An application of genetic algorithms to evolve Hopfield type optimum network architectures for object extraction 应用遗传算法进化Hopfield型最优网络结构进行目标提取
Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.489200
Susmita De, A. Ghosh, S. Pal
{"title":"An application of genetic algorithms to evolve Hopfield type optimum network architectures for object extraction","authors":"Susmita De, A. Ghosh, S. Pal","doi":"10.1109/ICEC.1995.489200","DOIUrl":"https://doi.org/10.1109/ICEC.1995.489200","url":null,"abstract":"Genetic Algorithms (GAS) have heen used l o evolve I iop j ie ld type oplimuni neural network archileciures for object background classijicatioii. Each chromosome of the GA represenis an archiieciure. The initial population is set randomly. The eirerqy t i d l l f : a i the converged state of each network is taken as its fitness. The best chromosome of I h e j i n a l generotion is taken io be the optimum network configuraiion. Tlie evolved networks are fo,iintl l o have less (compared lo t h e corresponding fixet’ fully connected version) connectivity f o r providing coniparnble oulp.uts.","PeriodicalId":213919,"journal":{"name":"Proceedings of 1995 IEEE International Conference on Evolutionary Computation","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125090226","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
Staff scheduling by a genetic algorithm with heuristic operators 基于启发式算子的遗传算法的员工调度
Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.489191
J. Tanomaru
{"title":"Staff scheduling by a genetic algorithm with heuristic operators","authors":"J. Tanomaru","doi":"10.1109/ICEC.1995.489191","DOIUrl":"https://doi.org/10.1109/ICEC.1995.489191","url":null,"abstract":"This paper has two major goals: to define a workforce scheduling problem with many realistic constraints, and to investigate its solution using a customized genetic algorithm featuring a group of operators which combine stochastic behavior and heuristics. Experiments show that high-quality workforce schedules can be obtained in reasonable time even for large problems.","PeriodicalId":213919,"journal":{"name":"Proceedings of 1995 IEEE International Conference on Evolutionary Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130530114","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: 51
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