Proceedings of 1995 IEEE International Conference on Evolutionary Computation最新文献_第2页

On the mean convergence time for simple genetic algorithms 简单遗传算法的平均收敛时间

Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.489176

T. Niwa, M. Tanaka

引用次数: 10

The application of evolution strategies to the problem of parameter optimization in fuzzy rulebased systems 演化策略在模糊规则系统参数优化问题中的应用

Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.487493

M. Fathi-Torbaghan, L. Hildebrand

{"title":"The application of evolution strategies to the problem of parameter optimization in fuzzy rulebased systems","authors":"M. Fathi-Torbaghan, L. Hildebrand","doi":"10.1109/ICEC.1995.487493","DOIUrl":"https://doi.org/10.1109/ICEC.1995.487493","url":null,"abstract":"Fuzzy logic has become widely acknowledged as an important and useful methodology in the design of rule based systems. It allows the representation of imprecise or incomplete knowledge and offers various mechanisms for reasoning with fuzzy data. In comparison to 'classical' rule based systems, only very few rules are needed to describe difficult problems. Nevertheless, in its current form it has several shortcomings: when it comes to the design of membership functions or to actually attaching priorities to the available rules, the choice of numerical quantities for the different parameters which is indispensable for the reasoning process is generally not justified by the results from knowledge acquisition and, what is worse, demands often a long process of iterative improvement to obtain good results. The use of empirically obtained quantitative representations seems questionable because of its high context dependence. The results are in many cases sub optimal systems. It seems natural to try to use a computer and an algorithmic optimization technique for the final adjustment of the parameters. Evolutionary algorithms seem especially appropriate for this task, partly because the fuzzy reasoning process can hardly be described by means of a closed mathematical formula-not to mention differentiability or other 'convenient' mathematical properties-partly because of the opportunity to apply parallel computation in a very natural way which seems essential in the design of large scale systems.","PeriodicalId":213919,"journal":{"name":"Proceedings of 1995 IEEE International Conference on Evolutionary Computation","volume":"67 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":"114721871","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

Applying logic grammars to induce sub-functions in genetic programming 应用逻辑语法诱导遗传规划中的子函数

Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.487477

M. Wong, K. Leung

{"title":"Applying logic grammars to induce sub-functions in genetic programming","authors":"M. Wong, K. Leung","doi":"10.1109/ICEC.1995.487477","DOIUrl":"https://doi.org/10.1109/ICEC.1995.487477","url":null,"abstract":"Genetic programming (GP) is a method of automatically inducing S-expressions in LISP to perform specified tasks. The problem of inducing programs can be reformulated as a search for a highly fit program in the space of all possible programs. This paper presents a framework in which the search space can be specified declaratively by a user. Its application in inducing sub-functions is detailed. The framework is based on a formalism of logic grammars and it is implemented as a system called LOGENPRO (LOgic grammar-based GENetic PROgramming system). The formalism is powerful enough to represent context-sensitive information and domain-dependent knowledge. This knowledge can be used to accelerate the learning speed and/or improve the quality of the programs induced. The system is also very flexible and programs in various programming languages can be acquired. Automatic discovery of sub-functions is one of the most important research areas in GP. An experiment is used to demonstrate that LOGENPRO can emulate Koza's (1992, 1994) automatically defined functions (ADF). Moreover, LOGENPRO can employ knowledge such as argument types in a unified framework. An experiment shows that LOGENPRO has a superior performance to that of ADF when more domain-dependent knowledge is available.","PeriodicalId":213919,"journal":{"name":"Proceedings of 1995 IEEE International Conference on Evolutionary Computation","volume":"8 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":"132215299","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: 49

Evolving radial basis function neural networks using a genetic algorithm 基于遗传算法的径向基函数神经网络进化

Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.489163

B. Carse, A. Pipe, T. Fogarty, T. Hill

{"title":"Evolving radial basis function neural networks using a genetic algorithm","authors":"B. Carse, A. Pipe, T. Fogarty, T. Hill","doi":"10.1109/ICEC.1995.489163","DOIUrl":"https://doi.org/10.1109/ICEC.1995.489163","url":null,"abstract":"Most research to date using genetic algorithms to evolve neural networks has focused on the multi-layer perceptron. Altemative neural network approaches such as the radial basis function network, and their representations appear to have received relatively little attention as grist for the GA mill. This is perhaps surprising since, for example, the radial basis function network has also been proved to be universal function approximator. Here we focus on evolution of radial basis function networks. While the multilayer perceptron network approximates functions through global interaction between network nodes, the radial basis function network uses local interactions between network nodes. It is suggested, that this difference may be of significance in tem of epistatic interactions in encoded genomes for the two types of network, which affects the ability of the genetic algorithm to evolve successful networks. A representation and attendant genetic operators for evolving radial basis function networks are proposed, drawing on recent work on evolutionary fuzzy logic systems. Experimental results in applying a hybrid leaming technique, using a genetic algorithm for evolving the radial basis function hidden layer (number of hidden nodes and hidden node centres and widths) and supervised leaming for tuning of network connection weights, are presented.","PeriodicalId":213919,"journal":{"name":"Proceedings of 1995 IEEE International Conference on Evolutionary Computation","volume":"181 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":"131514189","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

Selecting features with genetic algorithm in handwritten digit recognition 基于遗传算法的手写体数字识别特征选择

Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.489180

Weiquan Liu, Minghui Wang, Yixin Zhong

引用次数: 12

A multiprocessor scheduling scheme using problem-space genetic algorithms 基于问题空间遗传算法的多处理机调度方案

Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.489147

M. Dhodhi, I. Ahmad, Ishfaq Ahmad

引用次数: 40

Application of evolution program to resource demand optimisation in project planning 演化程序在项目规划资源需求优化中的应用

Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.489187

M. Pawlak

引用次数: 3

Scheduling cargo trains using genetic algorithms 利用遗传算法调度货运列车

Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.489149

V. Salim, X. Cai

引用次数: 18

Evolving building blocks for genetic algorithms using genetic engineering 利用基因工程进化遗传算法的构建模块

Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.489170

J. Gero, V. Kazakov

引用次数: 14

A hybrid genetic algorithm/fuzzy logic approach to manufacturing process control 一种混合遗传算法/模糊逻辑的制造过程控制方法

Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.487495

Bernd Freisleben, S. Strelen

引用次数: 9