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

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Stack-based genetic programming 基于堆栈的遗传规划
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.350025
Tim Perkis
{"title":"Stack-based genetic programming","authors":"Tim Perkis","doi":"10.1109/ICEC.1994.350025","DOIUrl":"https://doi.org/10.1109/ICEC.1994.350025","url":null,"abstract":"Some recent work in the field of genetic programming (GP) has been concerned with finding optimum representations for evolvable and efficient computer programs. This paper describes a new GP system in which target programs run on a stack-based virtual machine. The system is shown to have certain advantages in terms of efficiency and simplicity of implementation, and for certain problems, its effectiveness is shown to be comparable or superior to current methods.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"70 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":"115666732","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: 165
The use of genetic programming to build queries for information retrieval 利用遗传规划构建信息检索查询
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349905
F. Petry, B. Buckles, T. Sadasivan, D. Kraft
{"title":"The use of genetic programming to build queries for information retrieval","authors":"F. Petry, B. Buckles, T. Sadasivan, D. Kraft","doi":"10.1109/ICEC.1994.349905","DOIUrl":"https://doi.org/10.1109/ICEC.1994.349905","url":null,"abstract":"Genetic programming is applied to an information retrieval system in order to improve Boolean query formulation via relevance feedback. This approach brings together the concepts of information retrieval and genetic programming. Documents are viewed as vectors in index term space. A Boolean query, viewed as a parse tree, is an organism in the genetic programming sense. Through the mechanisms of genetic programming, the query is modified in order to improve precision and recall. Relevance feedback is incorporated, in part, via user defined measures over a trial set of documents. The fitness of a candidate query can be expressed directly as a function of the relevance of the retrieved set. Preliminary results based on a testbed are given. The form of the fitness function has a significant effect upon performance and the proper fitness functions take into account relevance based on topicality (and perhaps other factors).<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"32 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":"129404737","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: 77
Neuro-genetic truck backer-upper controller 神经遗传卡车后置控制器
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349969
Marc Schoenauer, E. Ronald
{"title":"Neuro-genetic truck backer-upper controller","authors":"Marc Schoenauer, E. Ronald","doi":"10.1109/ICEC.1994.349969","DOIUrl":"https://doi.org/10.1109/ICEC.1994.349969","url":null,"abstract":"The precise docking of a truck at a loading dock has been proposed in (Nguyen and Widrow, 1990) as a benchmark problem for non-linear control by neural-nets. The main difficulty is that backpropagation is not a priori suitable as a learning paradigm, because no set of training vectors is available: It is non-trivial to find solution trajectories that dock the truck from anywhere in the loading yard. In this paper we show how a genetic algorithm can evolve the weights of a feedforward 3-layer neural net that solves the control problem for a given starting state, achieving a short trajectory from starting point to goal. The fitness of a net in the population is a function of both the nearest position from the goal and the distance travelled. The influence of input data renormalisation on trajectory precision is also discussed.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"30 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":"129466326","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: 46
Genetic reinforcement learning for cooperative traffic signal control 基于遗传强化学习的交通信号协同控制
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.350012
S. Mikami, Y. Kakazu
{"title":"Genetic reinforcement learning for cooperative traffic signal control","authors":"S. Mikami, Y. Kakazu","doi":"10.1109/ICEC.1994.350012","DOIUrl":"https://doi.org/10.1109/ICEC.1994.350012","url":null,"abstract":"Optimization of a group of traffic signals over an area is a large, multi-agent-type real-time planning problem without a precise reference model being given. To do this planning, each signal should learn not only to acquire its control plans individually through reinforcement learning, but also to cooperate with other signals. These two objectives-distributed learning of agents and cooperation among agents-conflict with each other, and a method that blends these two objectives together is required. In the method proposed in this paper, these two objectives correspond to localized reinforcement learning and global combinatorial optimization, respectively, and the method thus achieves cooperation in the long term without bothering with autonomy. The outline of the idea is as follows: each agent performs reinforcement learning and reports its cumulative performance evaluation, and combinatorial optimization is simultaneously carried out to find appropriate parameters for long-term learning that maximize the total profit of the signals (agents).<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"67 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":"127626270","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: 96
A comparison of GA and RSNR docking 遗传算法与RSNR对接的比较
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349953
Yong L. Xiao, Donald E. Williams
{"title":"A comparison of GA and RSNR docking","authors":"Yong L. Xiao, Donald E. Williams","doi":"10.1109/ICEC.1994.349953","DOIUrl":"https://doi.org/10.1109/ICEC.1994.349953","url":null,"abstract":"Molecular docking calculations with genetic algorithms (GA) are compared with results calculated by a numeric random sampling Newton-Raphson (RSNR) method. The intermolecular interaction energy minimum is searched for using both a genetic algorithm approach and a numeric one for the docking process. Intermolecular interactions of a larger molecular complex of an anticancer drug have been investigated. The performance of GAs on molecular docking calculations is discussed and compared with the numerical method. The results of implementation indicate that the GA approach is superior to conventional methods used in energy minimization when there exist many local minima as well as a global minimum. The GA method, which is computationally more practical for applications to large biological systems, provides a rational approach to drug discovery and novel molecular structure design.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"16 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":"121036878","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: 1
Extended forking genetic algorithm for order representation (o-fGA) 有序表示的扩展分叉遗传算法
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349984
S. Tsutsui, Isao Hayashi, Y. Fujimoto
{"title":"Extended forking genetic algorithm for order representation (o-fGA)","authors":"S. Tsutsui, Isao Hayashi, Y. Fujimoto","doi":"10.1109/ICEC.1994.349984","DOIUrl":"https://doi.org/10.1109/ICEC.1994.349984","url":null,"abstract":"There are two types of GAs with difference of their representation of strings. They are the binary coded GA and the order-based GA. We've already proposed a new type of binary coded GA, called the forking GA (fGA), as a kind of multi-population GA and showed that the searching power of the fGA is superior to the standard GA. The distinguished feature of the fGA is that each population takes a different role in optimization. That is, each population is responsible for searching in a non-overlapping sub-area of the search space. In this paper, the extended forking GA for order representation, called the o-fGA, is proposed. The results of experiments for the blind traveling salesperson problem (TSP) show that the approach of fGA is also effective for the order representation.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"38 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":"117197072","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
The effect of population enrichment in genetic programming 种群富集在遗传规划中的作用
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349907
John E. Perry
{"title":"The effect of population enrichment in genetic programming","authors":"John E. Perry","doi":"10.1109/ICEC.1994.349907","DOIUrl":"https://doi.org/10.1109/ICEC.1994.349907","url":null,"abstract":"The paper examines the effect of \"population enrichment\" in genetic programming as a means of efficiently discovering promising directions for solution exploration in a large problem space. With genetic programming it is advantageous to not restrict the size or shape of the solution and enrichment offers an efficient way to present the initial population with interesting options for development. Multiple abbreviated runs were made, using different random seeds, to keep the size of the members small and save processing time. The best member from each abbreviated run was used to create an enriched population which was loaded along with a full complement of randomly generated unique members at the beginning of a consolidated run.<<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":"130696369","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: 20
Initial performance comparisons for the delta coding algorithm 增量编码算法的初始性能比较
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349911
Keith E. Mathias, L. D. Whitley
{"title":"Initial performance comparisons for the delta coding algorithm","authors":"Keith E. Mathias, L. D. Whitley","doi":"10.1109/ICEC.1994.349911","DOIUrl":"https://doi.org/10.1109/ICEC.1994.349911","url":null,"abstract":"Delta coding is an iterative genetic search strategy that sustains search by periodically re-initializing the population. This helps to avoid premature convergence during genetic search. Delta coding also remaps hyperspace with each iteration in an attempt to locate \"easier\" search spaces with respect to genetic search. Here, the optimization ability of delta coding is compared against the CHC genetic algorithm and a mutation driven stochastic hill-climbing algorithm on a suite of standard genetic algorithm test functions.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"14 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":"130780095","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: 28
Transforming the search space with Gray coding 用灰色编码变换搜索空间
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349897
Keith E. Mathias, L. D. Whitley
{"title":"Transforming the search space with Gray coding","authors":"Keith E. Mathias, L. D. Whitley","doi":"10.1109/ICEC.1994.349897","DOIUrl":"https://doi.org/10.1109/ICEC.1994.349897","url":null,"abstract":"Genetic algorithm test functions have typically been designed with properties in numeric space that make it difficult to locate the optimal solution using traditional optimization techniques. The use of Gray coding has been found to enhance the performance of genetic search in some cases. However, Gray coding produces a different function mapping that may have fewer local optima and different relative hyperplane relationships. Therefore, inferences about a function will not necessarily hold when transformed to another search space. In fact, empirical results indicate that some genetic algorithm test functions are significantly altered by Gray coding such that local optimization methods often perform better than genetic algorithms.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"83 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":"132210297","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: 80
NN's and GA's: evolving co-operative behaviour in adaptive learning agents 神经网络和遗传算法:自适应学习代理中不断进化的合作行为
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349937
Mukesh J. Patel, V. Maniezzo
{"title":"NN's and GA's: evolving co-operative behaviour in adaptive learning agents","authors":"Mukesh J. Patel, V. Maniezzo","doi":"10.1109/ICEC.1994.349937","DOIUrl":"https://doi.org/10.1109/ICEC.1994.349937","url":null,"abstract":"Without a comprehensive training set, it is difficult to train neural networks (NN) to solve a complex learning task. Usually, the more complex the problem or task the NNs have to learn, the less likely it is that there is a realistic training set that could be used for (supervised) training. One way to overcome this limitation is to implement an evolutionary approach to train NNs. We report the results of a novel implementation of an evolutionary computational technique, based on a modified genetic algorithm (GA), to evolve feedforward NN topologies and weight distributions. The learning task was for two fairly simple but autonomous agents (controlled by NNs) to learn to co-operate in order to accomplish a task. Given the complexity of the task, an evolutionary approach to a search for an appropriate NN topology and weight distribution seems to be a promising and viable approach, as our results show. The implications of the results are further discussed.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"13 4 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":"131573407","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: 14
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