发布求助
文献互助 智能选刊 最新文献

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

筛选
英文 中文
Clique finding-a genetic approach 派系发现——一种遗传方法
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.350049
P. Guturu, A. S. Murthy, V. Sastry
{"title":"Clique finding-a genetic approach","authors":"P. Guturu, A. S. Murthy, V. Sastry","doi":"10.1109/ICEC.1994.350049","DOIUrl":"https://doi.org/10.1109/ICEC.1994.350049","url":null,"abstract":"Presents a novel and efficient genetic approach for finding maximal cliques in a graph. A binary alphabet has been chosen to represent the presence or absence of nodes in a subgraph. The approach is to start with an initial population having small sized graphs, and then to effectively generate larger ones using a new crossover mechanism called 'partial copy crossover'. The splitting of the mutation operator into two operators, namely 'addition' and 'deletion', has been found to be effective for both increasing the diversity of the population and controlling the number of relevant subgraphs, i.e. those with the potentiality to become cliques. Experimental results on graphs with between 5 and 50 nodes and varying edge densities establish the efficacy and robustness of the approach.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"29 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":"127345868","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: 15
An empirical comparison of two evolutionary methods for satisfiability problems 求解可满足性问题的两种进化方法的实证比较
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349908
Jin-Kao Hao, R. Dorne
{"title":"An empirical comparison of two evolutionary methods for satisfiability problems","authors":"Jin-Kao Hao, R. Dorne","doi":"10.1109/ICEC.1994.349908","DOIUrl":"https://doi.org/10.1109/ICEC.1994.349908","url":null,"abstract":"The paper compares two evolutionary methods for model finding in the satisfiability problem (SAT): genetic algorithms (GAs) and the mask method (MASK). The main characteristics of these two methods are that both of them are population-based, and use binary representation. Great care is taken to make sure that the same SAT instances and the same criteria are used in the comparison. Results indicate that MASK greatly outperforms GAs in the sense that MASK manages to deal with harder SAT instances at a lower cost.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"178 9 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":"123668810","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
Finding maximum flow with random and genetic search 通过随机和遗传搜索找到最大流量
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349936
Mark F. Bramlette
{"title":"Finding maximum flow with random and genetic search","authors":"Mark F. Bramlette","doi":"10.1109/ICEC.1994.349936","DOIUrl":"https://doi.org/10.1109/ICEC.1994.349936","url":null,"abstract":"Solving a maximum flow problem requires finding the greatest balanced flow from a source to a sink in a weighted directional graph. In balanced flow, each node's total input and total output are equal. This paper compares one random and two genetic approaches to finding such solutions. The representation of candidate solutions guarantees balanced flow in all products of mutation and crossover. The method of solution uses a stochastic search (random or genetic) to insure that no link is over capacity, no node has excess output, and each allocation is an integer. Then it achieves balance through a fast deterministic search to remove excess input. This method solved a sample problem in about one-ninth as many generations as a genetic search using penalty functions.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"125 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":"114954783","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
A knowledge-based genetic heuristic for learning certainty factors 基于知识的遗传启发式学习确定性因素
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.350029
Douglas B. Lynch, D. Kuncicky
{"title":"A knowledge-based genetic heuristic for learning certainty factors","authors":"Douglas B. Lynch, D. Kuncicky","doi":"10.1109/ICEC.1994.350029","DOIUrl":"https://doi.org/10.1109/ICEC.1994.350029","url":null,"abstract":"An expert network is a type of inference network that is derived from an expert system. One of the uses of expert networks is to to refine measures of certainty in knowledge bases using neural network learning techniques. Goal-directed Monte Carlo search (GDMC) is a parallel stochastic hillclimbing method that is being successfully used to refine certainty factors from data. This paper presents a new heuristic for GDMC that improves its performance by incorporating genetic algorithm techniques.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"19 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":"128766098","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
Using real-valued genetic algorithms to evolve rule sets for classification 利用实值遗传算法进化分类规则集
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.350030
A. Corcoran, S. Sen
{"title":"Using real-valued genetic algorithms to evolve rule sets for classification","authors":"A. Corcoran, S. Sen","doi":"10.1109/ICEC.1994.350030","DOIUrl":"https://doi.org/10.1109/ICEC.1994.350030","url":null,"abstract":"In this paper, we use a genetic algorithm to evolve a set of classification rules with real-valued attributes. We show how real-valued attribute ranges could be encoded with real-valued genes and present a new uniform method for representing don't cares in the rules. We view supervised classification as an optimization problem, and evolve rule sets that maximize the number of correct classifications of input instances. We use a variant of the Pitt approach to genetic-based machine learning system with a novel conflict resolution mechanism between competing rules within the same rule set. Experimental results demonstrate the effectiveness of our proposed approach on a benchmark wine classifier system.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"28 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":"126751384","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: 170
Multi-population evolution strategies for structural image analysis 结构图像分析的多种群进化策略
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.350011
H. Füger, G. Stein, Uwe Stilla
{"title":"Multi-population evolution strategies for structural image analysis","authors":"H. Füger, G. Stein, Uwe Stilla","doi":"10.1109/ICEC.1994.350011","DOIUrl":"https://doi.org/10.1109/ICEC.1994.350011","url":null,"abstract":"To identify objects in aerial images, a special structural approach, based on a blackboard system, is used. The reference objects are described with generic models and a set of real-valued parameters. To adapt these parameters in an automatical way, a closed-loop system is proposed using multi-population evolution strategies with a special form of migration. The result of the parameter optimization is demonstrated with an example of identifying bridges in aerial images. By applying this closed-loop system, a reduction of computational effort was achieved.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"27 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":"126870056","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
A genetic algorithm for file and task placement in a distributed system 分布式系统中文件和任务放置的遗传算法
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349928
A. Corcoran, D. Schoenefeld
{"title":"A genetic algorithm for file and task placement in a distributed system","authors":"A. Corcoran, D. Schoenefeld","doi":"10.1109/ICEC.1994.349928","DOIUrl":"https://doi.org/10.1109/ICEC.1994.349928","url":null,"abstract":"We explore the distributed file and task placement problem, which is intractable. We also discuss genetic algorithms and how they have been used successfully to solve combinatorial problems. Our experimental results show the GA to be far superior to the greedy heuristic in obtaining optimal and near optimal file and task placements for the problem with various data sets.<<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":"123872150","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
Evolutionary computation approaches to halftoning algorithm 半调算法的进化计算方法
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349956
H. Saito, Naoki Kobayashi
{"title":"Evolutionary computation approaches to halftoning algorithm","authors":"H. Saito, Naoki Kobayashi","doi":"10.1109/ICEC.1994.349956","DOIUrl":"https://doi.org/10.1109/ICEC.1994.349956","url":null,"abstract":"In this paper, evolutionary computation approaches to halftoning algorithm is described. The halftoning technique is required for representing gray-tone images on bilevel displays or printers. Using the halftoning techniques, gray-tone images are transformed into binary representations. In general, visually pleasing halftone images must have high spatial and gray-level resolution. In this study, the halftoning algorithm is regarded as a optimization problem that is to search for the visually pleasing placement of black pixel in the halftoning image. For solving the problem, a Simple GA (SGA) and a Single Populated GA (SPGA) are applied to the halftoning algorithm. Some halftone images obtained by the evolutionary computation are shown, and their performances are demonstrated.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"294 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":"124223672","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: 10
Parallel evolution of communicating classifier systems 通信分类系统的并行进化
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349976
L. Bull, T. Fogarty
{"title":"Parallel evolution of communicating classifier systems","authors":"L. Bull, T. Fogarty","doi":"10.1109/ICEC.1994.349976","DOIUrl":"https://doi.org/10.1109/ICEC.1994.349976","url":null,"abstract":"We present an architecture that allows the division of a search space and the parallel solution of the resulting sub-problems. We use multiple genetic algorithms to evolve communicating classifier systems, where each classifier system represents a sub-system of the complete task. Any communication is uninterpreted and emergent to the system, indicating structure and interdependence between the sub-problems. A simulated trail following task, with three communicating classifier systems, is used to demonstrate the approach and we compare its performance to that of an equivalent single classifier system responsible for the whole problem.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"301 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":"123195101","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: 13
An analysis of crossover's effect in genetic algorithms 遗传算法中交叉效应分析
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349989
M. Yamamura, H. Satoh, S. Kobayashi
{"title":"An analysis of crossover's effect in genetic algorithms","authors":"M. Yamamura, H. Satoh, S. Kobayashi","doi":"10.1109/ICEC.1994.349989","DOIUrl":"https://doi.org/10.1109/ICEC.1994.349989","url":null,"abstract":"The crossover operation is characteristic of genetic algorithms (GAs). This paper analyzes the crossover effect in GAs. We start with two bits, that is the minimum chromosome length to crossover. We compare one operator GAs, using only selection, and two operators GAs by selection and crossover with respect to the expected quality and speed of the convergence. First, we analyse the case of two individuals, that is the minimum population size, by a Markov chain. We show the boundary in the fitness assignment cube where crossover improves the absorption probability to the optimum. We also show that crossover always speeds up convergence. Second, we analyse the larger population case by numerically solving the difference equations. We show a boundary where the crossover speeds up convergence. Normal medium sized GAs can be positioned between these two extremes.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"88 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":"116322013","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
下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信