发布求助
文献互助 智能选刊 最新文献

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

筛选
英文 中文
Sizing the population with respect to the local progress in (1,/spl lambda/)-evolution strategies-a theoretical analysis 根据(1，/spl lambda/)的局部进展对种群进行评估——进化策略——一个理论分析
Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.489123
N. Hansen, A. Gawelczyk, A. Ostermeier
{"title":"Sizing the population with respect to the local progress in (1,/spl lambda/)-evolution strategies-a theoretical analysis","authors":"N. Hansen, A. Gawelczyk, A. Ostermeier","doi":"10.1109/ICEC.1995.489123","DOIUrl":"https://doi.org/10.1109/ICEC.1995.489123","url":null,"abstract":"This paper presents an analysis of the local serial rate of progress with respect to the number of offspring X for the (1,X)-evolution strategy. It is shown that local serial progress is maximized when the expected progress of the second best offspring is zero. The theoretical results lead to a simple but efficient adaptation rule for A, which needs no extra fitness function evaluations and only small computational expense. Simulations of the A-adaptation on simple test functions are shown.","PeriodicalId":213919,"journal":{"name":"Proceedings of 1995 IEEE International Conference on Evolutionary Computation","volume":"16 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":"121383590","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
An integrated framework for devising optimum generation schedules 设计最佳发电计划的综合框架
Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.489109
D. Srinivasan, A. Tettamanzi
{"title":"An integrated framework for devising optimum generation schedules","authors":"D. Srinivasan, A. Tettamanzi","doi":"10.1109/ICEC.1995.489109","DOIUrl":"https://doi.org/10.1109/ICEC.1995.489109","url":null,"abstract":"An integrated framework for generating optimum unit commitment and dispatch schedules is presented in this paper. The work reported here employs a hybrid technique by which a genetic population can be confined to a set of feasible solutions. Heuristics are used to ensure that all the constraints, both linear and nonlinear, are fuQilled for each member of the population. The use of this technique, which combines the advantages of knowkdgebased methods with the strengths of evolutionary algorithms, results in consideruble reduction in computing time, making its application viable in daily operation scheduling.","PeriodicalId":213919,"journal":{"name":"Proceedings of 1995 IEEE International Conference on Evolutionary Computation","volume":"21 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":"121076096","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
Hybridized crossover-based search techniques for program discovery 基于杂交交叉的程序发现搜索技术
Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.487447
Una-May O’Reilly, F. Oppacher
{"title":"Hybridized crossover-based search techniques for program discovery","authors":"Una-May O’Reilly, F. Oppacher","doi":"10.1109/ICEC.1995.487447","DOIUrl":"https://doi.org/10.1109/ICEC.1995.487447","url":null,"abstract":"Addresses the problem of program discovery as defined by genetic programming. By combining a hierarchical crossover operator with two traditional single-point search algorithms (simulated annealing and stochastic iterated hill climbing), we have solved some problems by processing fewer candidate solutions and with a greater probability of success than genetic programming. We have also enhanced genetic programming by hybridizing it with the simple idea of hill climbing from a few individuals, at a fixed interval of generations.","PeriodicalId":213919,"journal":{"name":"Proceedings of 1995 IEEE International Conference on Evolutionary Computation","volume":"285 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":"127844048","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: 66
Genetic algorithm based pattern allocation schemes for training set parallelism in backpropagation neural networks 基于遗传算法的反向传播神经网络训练集并行性模式分配方案
Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.487442
S. K. Foo, P. Saratchandran, N. Sundararajan
{"title":"Genetic algorithm based pattern allocation schemes for training set parallelism in backpropagation neural networks","authors":"S. K. Foo, P. Saratchandran, N. Sundararajan","doi":"10.1109/ICEC.1995.487442","DOIUrl":"https://doi.org/10.1109/ICEC.1995.487442","url":null,"abstract":"Training set parallelization is an efficient method to optimize the training procedure performance of the backpropagation neural network algorithm. In training set parallelism, the training patterns are distributed 'optimally' among a heterogeneous array of processors, optimality criterion obtain the minimum training time per epoch. Earlier studies on heterogeneous transputers connected in a pipeline-ring topology have indicated that the above optimization problem results in a mixed integer programming problem and results in large computation time to find the optimal pattern allocations. In this paper, a genetic algorithm is used as an optimization tool to find the optimal allocation of patterns. The approach is illustrated using two benchmark problems, the 256-8-256 Encoder and NETTALK problems. Results indicate that when 'a priori' information is not used, the computation time needed by the genetic algorithm is comparable to that obtained by mixed integer programming. However, when 'a priori' information is used, the genetic algorithm results in significant reduction in computation time for finding the optimal solution.","PeriodicalId":213919,"journal":{"name":"Proceedings of 1995 IEEE International Conference on Evolutionary Computation","volume":"44 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":"127364591","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
MOGA: multi-objective genetic algorithms 多目标遗传算法
Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.489161
T. Murata, H. Ishibuchi
{"title":"MOGA: multi-objective genetic algorithms","authors":"T. Murata, H. Ishibuchi","doi":"10.1109/ICEC.1995.489161","DOIUrl":"https://doi.org/10.1109/ICEC.1995.489161","url":null,"abstract":"In this paper, we propose a .framework of genetic algorithms to search for Pareto optimal solutions (i.e., non-dominated solutions) of multi-ohjectiv,e optimizution problems. Our approuch d!fers from single-objective genetic algorithms in its selection proceduiae and elite preserve strategy. The selection procedure in our genetic algorithms selects individuals for a cromover operation based on a weighted sum of multiple ohjective functions. The characteristic feature of the selection procedure is that the weights attached to the multiple objective ,functions are not constant but rundomly specified for each selection. 7he elite preserve strategy in our genetic algorithms uses multiple elite solutions instead of a single eliie solution. That is, a certain number of individuals are selected from a tentative set of Pareto optimal solutions and inherited to the next generation as elite individuals.","PeriodicalId":213919,"journal":{"name":"Proceedings of 1995 IEEE International Conference on Evolutionary Computation","volume":"16 6 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":"125835497","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: 471
An evolutionary approach for frequency assignment in cellular radio networks 蜂窝无线网络频率分配的一种进化方法
Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.487441
R. Dorne, Jin-Kao Hao
{"title":"An evolutionary approach for frequency assignment in cellular radio networks","authors":"R. Dorne, Jin-Kao Hao","doi":"10.1109/ICEC.1995.487441","DOIUrl":"https://doi.org/10.1109/ICEC.1995.487441","url":null,"abstract":"This paper presents a study of evolutionary algorithms (EAs) for a real application: the frequency assignment problem (FAP) in cellular radio networks. This problem is of great importance both in practice and in theory. In practice, solving this problem efficiently will allow the telecommunications operator to manage larger and larger cellular networks. In theory, the simplification of FAP is reduced to the graph coloring problem which is NP-complete. In our work, we take a progressive approach: first, we study separately the different components of EAs in order to understand the interest of each of them for our application; then, we design hybrid EAs which integrate efficient techniques (local search, constraint programming, etc.) into evolutionary operators. Experiments using our approach on real-size FAP instances (up to 300 cells and 13000 constraints) give very encouraging results. Direct comparison of our approach with simulated annealing (SA), constraint programming (CP) and graph coloring algorithms on the same set of tests shows the strong interest of our hybrid evolutionary approach for this application.","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":"130065916","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: 57
Algorithm evolution for face recognition: what makes a picture difficult 人脸识别的算法进化:是什么让图片变得困难
Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.487453
A. Teller, M. Veloso
{"title":"Algorithm evolution for face recognition: what makes a picture difficult","authors":"A. Teller, M. Veloso","doi":"10.1109/ICEC.1995.487453","DOIUrl":"https://doi.org/10.1109/ICEC.1995.487453","url":null,"abstract":"One of the classic problems in computer vision is the face recognition problem. In general, this problem can take on a wide variety of forms, but the most common face recognition problem is \"Who is this a picture of?\" Evolutionary computation has, in the past, been applied indirectly to this problem through techniques like learning neural networks. This paper introduces a genetic programming style approach to learning algorithms that directly investigate face images and are coordinated into a face recognition system. Through a series of experiments, we show that evolved algorithms can accomplish the face recognition task. We also highlight several pitfalls and misconceptions surrounding face recognition as a learning problem.","PeriodicalId":213919,"journal":{"name":"Proceedings of 1995 IEEE International Conference on Evolutionary Computation","volume":"69 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":"128854883","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: 40
Phenotypic forking genetic algorithm (p-fGA) 表型分叉遗传算法(p-fGA)
Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.487446
S. Tsutsui, Y. Fujimoto
{"title":"Phenotypic forking genetic algorithm (p-fGA)","authors":"S. Tsutsui, Y. Fujimoto","doi":"10.1109/ICEC.1995.487446","DOIUrl":"https://doi.org/10.1109/ICEC.1995.487446","url":null,"abstract":"Proposes a new type of multi-population genetic algorithm, the p-fGA (phenotypic forking GA), an extension of the previously proposed g-fGA (genotype forking GA). Both the g-fGA and the p-fGA are designed to solve multi-modal problems which are difficult to solve by traditional GAs. We use multi-population schemes that include one parent population with a blocking mode and one or more child populations with a shrinking mode. The g-fGA defines its sub-space for each population by a \"salient schema\" within the genotypic search space. In contrast to this, the p-fGA defines its sub-space by a \"neighborhood hypercube\" around the current best individual in the phenotypic search space. Empirical results show that the p-fGA has a fairly good performance, as does the g-fGA, and the variable-resolution p-fGA has the capability of searching with high resolution and can improve on the local search capability in a genetic search.","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":"133800447","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
A genetic algorithm for the induction of pushdown automata 下推自动机的一种遗传算法
Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.487478
M. Lankhorst
{"title":"A genetic algorithm for the induction of pushdown automata","authors":"M. Lankhorst","doi":"10.1109/ICEC.1995.487478","DOIUrl":"https://doi.org/10.1109/ICEC.1995.487478","url":null,"abstract":"Presents a genetic algorithm used to infer pushdown automata from legal and illegal examples of a language. It describes the type of automaton that is used, the evaluation of the fitness of automata with respect to a set of examples of a language, the representation of automata in the genetic algorithm, and the genetic operators that work on this representation. Results are reported on the inference of a test suite of 10 languages. Pushdown automata for the language of correctly balanced and nested parentheses expressions, the language of sentences containing an equal number of a's and b's, the two-symbol palindromes, a set of regular languages and a small natural language subset were inferred. Furthermore, some possible improvements and extensions of the algorithm are discussed.","PeriodicalId":213919,"journal":{"name":"Proceedings of 1995 IEEE International Conference on Evolutionary Computation","volume":"28 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":"132484690","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
Reinforcement learning method for generating fuzzy controller 模糊控制器生成的强化学习方法
Proceedings of 1995 IEEE International Conference on Evolutionary Computation Pub Date : 1995-11-29 DOI: 10.1109/ICEC.1995.489158
T. Fukuda, Y. Hasegawa, K. Shimojima, F. Saito
{"title":"Reinforcement learning method for generating fuzzy controller","authors":"T. Fukuda, Y. Hasegawa, K. Shimojima, F. Saito","doi":"10.1109/ICEC.1995.489158","DOIUrl":"https://doi.org/10.1109/ICEC.1995.489158","url":null,"abstract":"In this paper, we propose a new reinforcement learning algorithm for generating a fuzzy controller. The algorithm generates a range of continuous real-valued actions, and reinforcement signal is self-scaled. This prevents the weights from overshooting when the system gets a very large reinforcement value. The proposed method is applied to the problem of controlling the brachiation robot, which moves dynamically from branch to branch like a gibbon swinging its body in ii pendulum fashion(Fig.1).","PeriodicalId":213919,"journal":{"name":"Proceedings of 1995 IEEE International Conference on Evolutionary Computation","volume":"93 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":"130646584","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: 22
首页 上一页
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
小红书