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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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VLSI circuit synthesis using a parallel genetic algorithm 利用并行遗传算法合成VLSI电路
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.350033
Mike Davis, Luoping Liu, J. G. Elias
{"title":"VLSI circuit synthesis using a parallel genetic algorithm","authors":"Mike Davis, Luoping Liu, J. G. Elias","doi":"10.1109/ICEC.1994.350033","DOIUrl":"https://doi.org/10.1109/ICEC.1994.350033","url":null,"abstract":"A parallel implementation of a genetic algorithm used to evolve simple analog VLSI circuits is described. The parallel computer system consisted of twenty distributed SPARC workstations whose computational activity is controlled by the parallel environment coordination language Linda. Work-in-progress on using the parallel GA to realize optimized circuits and to discover new types of equivalent-function circuits is presented. The use of biologically inspired development rules to limit the scope of circuits generated by recombination operators to circuits that have an increased chance of surviving is briefly discussed.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"8 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114031299","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
A niched Pareto genetic algorithm for multiobjective optimization 多目标优化的小生境Pareto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.1994.350037
Jeffrey D. Horn, Nicholas Nafpliotis, D. Goldberg
{"title":"A niched Pareto genetic algorithm for multiobjective optimization","authors":"Jeffrey D. Horn, Nicholas Nafpliotis, D. Goldberg","doi":"10.1109/ICEC.1994.350037","DOIUrl":"https://doi.org/10.1109/ICEC.1994.350037","url":null,"abstract":"Many, if not most, optimization problems have multiple objectives. Historically, multiple objectives have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic algorithm (GA), however, is readily modified to deal with multiple objectives by incorporating the concept of Pareto domination in its selection operator, and applying a niching pressure to spread its population out along the Pareto optimal tradeoff surface. We introduce the Niched Pareto GA as an algorithm for finding the Pareto optimal set. We demonstrate its ability to find and maintain a diverse \"Pareto optimal population\" on two artificial problems and an open problem in hydrosystems.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"95 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":"114817390","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: 2753
Convergence of non-elitist strategies
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.350041
G. Rudolph
{"title":"Convergence of non-elitist strategies","authors":"G. Rudolph","doi":"10.1109/ICEC.1994.350041","DOIUrl":"https://doi.org/10.1109/ICEC.1994.350041","url":null,"abstract":"The paper offers sufficient conditions to prove global convergence of non-elitist evolutionary algorithms. If these conditions can be applied they yield bounds of the convergence rate as a by-product. This is demonstrated by an example that can be calculated exactly.<<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":"127756254","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: 73
Application of evolutionary programming to optimal reactive power dispatch 演化规划在无功优化调度中的应用
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349966
J. T. Ma, Q. Wu
{"title":"Application of evolutionary programming to optimal reactive power dispatch","authors":"J. T. Ma, Q. Wu","doi":"10.1109/ICEC.1994.349966","DOIUrl":"https://doi.org/10.1109/ICEC.1994.349966","url":null,"abstract":"This paper is concerned with application of evolutionary programming to optimal reactive power dispatch and voltage control of power systems. Several techniques have been developed to make the EP suitable to the practical optimal problems of large scale systems. Simulation results, compared with those obtained by using a conventional gradient-based optimization method, are presented to show the potential for applications of the proposed method to power system economical and secure operations.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"39 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":"121725494","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
A delayed-action classifier system for learning in temporal environments 在时间环境下学习的延迟动作分类器系统
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349978
B. Carse, T. Fogarty
{"title":"A delayed-action classifier system for learning in temporal environments","authors":"B. Carse, T. Fogarty","doi":"10.1109/ICEC.1994.349978","DOIUrl":"https://doi.org/10.1109/ICEC.1994.349978","url":null,"abstract":"This paper describes a modified version of the traditional classifier system called the Delayed Action Classifier System (DACS) which has been conceived for learning in environments that exhibit a rich temporal structure. DACS operates by delaying the action of appropriately tagged classifiers (called 'delayed-action classifiers') by a number of execution cycles which is encoded on the action part of these classifiers. This modification allows the rule discovery strategy, in many instances a genetic algorithm, to simultaneously explore the spaces of action (what to do) and time (when to do it). Results of initial experiments, which appear encouraging, of applying DACS to a prediction problem are presented, and the possible application of the delayed-action idea to learning in real-time environments is discussed.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"10 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":"131572592","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
ENZO-II-a powerful design tool to evolve multilayer feed forward networks enzo - ii -一个强大的设计工具，发展多层前馈网络
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349939
H. Braun, Peter Zagorski
{"title":"ENZO-II-a powerful design tool to evolve multilayer feed forward networks","authors":"H. Braun, Peter Zagorski","doi":"10.1109/ICEC.1994.349939","DOIUrl":"https://doi.org/10.1109/ICEC.1994.349939","url":null,"abstract":"ENZO-II combines two successful search techniques: gradient descent for an efficient local weight optimization and evolution for a global topology optimization. By using these, it takes full advantage of the efficiently computable gradient information without being trapped by local minima. Through knowledge transfer by inheriting parental weights, learning is speeded up by 1-2 orders of magnitude, and the expected fitness of the offspring is far above the average for this network topology. Moreover, ENZO-II impressively thins out the topology by the cooperation between a discrete mutation operator and a continuous weight decay method. Especially, ENZO-II also tries to cut off the connections to possibly redundant input units. Therefore, ENZO-II not only supports the user in the network design but also recognizes redundant input units.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"41 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":"131058775","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
Automated learning of a detector for the cores of /spl alpha/-helices in protein sequences via genetic programming 基于遗传编程的蛋白质序列/spl α /-螺旋核心检测器的自动学习
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349904
S. Handley
{"title":"Automated learning of a detector for the cores of /spl alpha/-helices in protein sequences via genetic programming","authors":"S. Handley","doi":"10.1109/ICEC.1994.349904","DOIUrl":"https://doi.org/10.1109/ICEC.1994.349904","url":null,"abstract":"The author used J.R. Koza's (1992) genetic programming to evolve programs that classified contiguous regions of proteins as being /spl alpha/-helix cores or not. He snipped positive and negative examples of /spl alpha/-helix core regions out of a set of 90 proteins. These proteins were chosen from the Brookhaven Protein Data Bank to be non-homologous. The fitness of the programs was defined as the correlation coefficient between the observed and the predicted /spl alpha/-helicity of the above regions. The fittest program produced by the genetic programming system that predicted the training set at least as well as the testing set had a correlation of 0.4818 between the observed classifications and the classifications predicted by the program (on the proteins in the testing set).<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"60 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":"124188668","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
Dynamic scheduling of computer tasks using genetic algorithms 基于遗传算法的计算机任务动态调度
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349947
C. Pico, R. L. Wainwright
{"title":"Dynamic scheduling of computer tasks using genetic algorithms","authors":"C. Pico, R. L. Wainwright","doi":"10.1109/ICEC.1994.349947","DOIUrl":"https://doi.org/10.1109/ICEC.1994.349947","url":null,"abstract":"We concentrate on non-preemptive hard real-time scheduling algorithms. We compare FIFO, EDLF, SRTF and genetic algorithms for solving this problem. The objective of the scheduling algorithm is to dynamically schedule as many tasks as possible such that each task meets its execution deadline, while minimizing the total delay time of all of the tasks. We present a MicroGA that uses a small population size of 10 chromosomes, running for 10 trials using a rather high mutation rate with a sliding window of 10 tasks. The steady-state GA was determined to be better than the generational GA for our MicroGA. We also present a parallel MicroGA model designed for parallel processors. The parallel MicroGA works best when migration is used to move tasks from one processor to another to even out the load as much a possible. Test cases show that the sequential MicroGA model and the parallel MicroGA model produced superior task schedules compared to other algorithms tested.<<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":"124293682","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: 30
Learning monitoring strategies: a difficult genetic programming application 学习监控策略:一个困难的遗传规划应用
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349931
M. Atkin, P. Cohen
{"title":"Learning monitoring strategies: a difficult genetic programming application","authors":"M. Atkin, P. Cohen","doi":"10.1109/ICEC.1994.349931","DOIUrl":"https://doi.org/10.1109/ICEC.1994.349931","url":null,"abstract":"Finding optimal or at least good monitoring strategies is an important consideration when designing an agent. We have applied genetic programming to this task, with mixed results. Since the agent control language was kept purposefully general, the set of monitoring strategies constitutes only a small part of the overall space of possible behaviors. Because of this, it was often difficult for the genetic algorithm to evolve them, even though their performance was superior. These results raise questions as to how easy it will be for genetic programming to scale up as the areas it is applied to become more complex.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"48 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":"116919310","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
An evolutionary approach to shape emergence 一种进化的方法来塑造出现
Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI: 10.1109/ICEC.1994.349958
M. Yan, Ruwei Dai
{"title":"An evolutionary approach to shape emergence","authors":"M. Yan, Ruwei Dai","doi":"10.1109/ICEC.1994.349958","DOIUrl":"https://doi.org/10.1109/ICEC.1994.349958","url":null,"abstract":"Shape emergence is a recognized visual phenomenon experienced by visually all humans. It involves perception of emergent shapes that only implicitly exist in a primary shape (an already interpreted shape), and do not directly correspond to the entities or subshapes used to construct the primary shape. This paper presents an evolutionary approach to shape emergence. Its basic idea is to maintain a population of shapes that aggregate with each other to produce their offspring shapes. The shape population evolves from one generation to another as new shapes are produced and old shapes are eliminated. The evolution is governed by a fitness function and a set of local aggregating rules. Since offspring shapes are always larger in size than their parent shapes, the evolution finally stops with a quiescent population in which no shape can produce any further offspring because of the limitation of the global boundary of the primary shape. The last several generations of shapes in the evolving process provide a vocabulary for emergent interpretations of the primary shape.<<ETX>>","PeriodicalId":393865,"journal":{"name":"Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence","volume":"39 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":"116476768","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
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