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Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)最新文献

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The impact of noise on iterated prisoner's dilemma with multiple levels of cooperation 噪声对多层次合作下迭代囚徒困境的影响
Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753) Pub Date : 2004-06-19 DOI: 10.1109/CEC.2004.1330878
S. Y. Chong, X. Yao
{"title":"The impact of noise on iterated prisoner's dilemma with multiple levels of cooperation","authors":"S. Y. Chong, X. Yao","doi":"10.1109/CEC.2004.1330878","DOIUrl":"https://doi.org/10.1109/CEC.2004.1330878","url":null,"abstract":"Real world dilemma rarely involved just two choices and perfect interactions without mistakes. In extending the realism of the iterated prisoner's evolutionary approaches included intermediate choices or mistakes (noise). This study takes a step further using a coevolving population of neural networks playing the IPD game with both intermediate choices and noise. Several issues will be addressed, which include the evolution of cooperation and the evolutionary stability in the presence of noise and more choices. Our experimental study shows that noise has a negative impact on the evolution of cooperation, but could improve, surprisingly, the evolutionary stability.","PeriodicalId":152088,"journal":{"name":"Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)","volume":"467 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123879862","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
Modeling coevolutionary genetic algorithms on two-bit landscapes: partnering strategies 二进制景观上的共同进化遗传算法建模:伙伴策略
Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753) Pub Date : 2004-06-19 DOI: 10.1109/CEC.2004.1331191
Ming Chang, K. Ohkura, K. Ueda, M. Sugiyama
{"title":"Modeling coevolutionary genetic algorithms on two-bit landscapes: partnering strategies","authors":"Ming Chang, K. Ohkura, K. Ueda, M. Sugiyama","doi":"10.1109/CEC.2004.1331191","DOIUrl":"https://doi.org/10.1109/CEC.2004.1331191","url":null,"abstract":"Different from standard genetic algorithms where each individual is evaluated separately according to predefined objective function(s), one most notable characteristic of coevolutionary genetic algorithms (CGA) is that evaluation procedures require more than one individual and an individual's fitness is depending on its interactions with its partners. In consequence, the implemented partnering strategies can have significant effects on the dynamical behaviour of CGA as well as their optimization performance. Infinite population models of CGA consisting of two populations coevolving on two-bit landscapes are described and investigated in the context of four well-applied partnering strategies. It is shown that even in these simplest models, the dynamical behaviour of CGA changes dramatically according to different evolutionary scenarios that deserves our attention from the perspective of coevolutionary algorithms designing.","PeriodicalId":152088,"journal":{"name":"Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)","volume":"13 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128471090","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: 2
Cascaded GP models for data mining 用于数据挖掘的级联GP模型
Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753) Pub Date : 2004-06-19 DOI: 10.1109/CEC.2004.1331178
P. Lichodzijewski, M. Heywood, A. N. Zincir-Heywood
{"title":"Cascaded GP models for data mining","authors":"P. Lichodzijewski, M. Heywood, A. N. Zincir-Heywood","doi":"10.1109/CEC.2004.1331178","DOIUrl":"https://doi.org/10.1109/CEC.2004.1331178","url":null,"abstract":"The cascade architecture for incremental learning is demonstrated within the context of genetic programming. Such a scheme provides the basis for building steadily more complex models until a desired degree of accuracy is reached. The architecture is demonstrated for several data mining datasets. Efficient training on standard computing platforms is retained using the RSS-DSS algorithm for stochastically sampling datasets in proportion to exemplar 'difficulty' and 'age'. Finally, the ensuing empirical study provides the basis for recommending the utility of sum square cost functions in the datasets considered.","PeriodicalId":152088,"journal":{"name":"Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117328721","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: 5
A hybrid swarm optimizer for efficient parameter estimation 一种高效参数估计的混合群优化器
Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753) Pub Date : 2004-06-19 DOI: 10.1109/CEC.2004.1330872
Santhoji Katare, A. Kalos, David H. West
{"title":"A hybrid swarm optimizer for efficient parameter estimation","authors":"Santhoji Katare, A. Kalos, David H. West","doi":"10.1109/CEC.2004.1330872","DOIUrl":"https://doi.org/10.1109/CEC.2004.1330872","url":null,"abstract":"This paper proposes a hybrid algorithm for parameter estimation - a population-based, stochastic, particle swarm optimizer to identify promising regions of search space that are further locally explored by a Levenburg-Marquardt optimizer. This hybrid method is able to find global optimum for six benchmark problems. It is sensitive to the swarm topology which defines information transfer between particles; however, the hypothesis (Kennedy et al., 2001) that a star topology is better for finding the optimum for problems with large number of optima is not supported by this study. It is also seen that in the absence of the local optimizer, particle swarm alone is not as effective. The proposed method is also demonstrated on an identical catalytic reactor model.","PeriodicalId":152088,"journal":{"name":"Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115411110","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: 39
Subspace FDC for sharing distance estimation 共享距离估计的子空间FDC
Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753) Pub Date : 2004-06-19 DOI: 10.1109/CEC.2004.1331105
Jian Zhang, Xiaohui Yuan, B. Buckles
{"title":"Subspace FDC for sharing distance estimation","authors":"Jian Zhang, Xiaohui Yuan, B. Buckles","doi":"10.1109/CEC.2004.1331105","DOIUrl":"https://doi.org/10.1109/CEC.2004.1331105","url":null,"abstract":"Niching techniques diversify the population of evolutionary algorithms, encouraging heterogeneous convergence to multiple optima. The key to an effective diversification is identifying the similarity among individuals. With no prior knowledge of the fitness landscapes, it is usually determined by uninformative assumptions on the number of peaks. We propose a method to estimate the sharing distance and the corresponding population size. Using the probably approximately correct (PAC) learning theory and the e-cover concept, we derive the PAC neighbor distance of a local optimum. Within this neighborhood, uniform samples are drawn and we compute the subspace fitness distance correlation (FDC) coefficients. An algorithm is developed to estimate the granularity feature of the fitness landscapes. The sharing distance is determined from the granularity feature and furthermore, the population size is decided. Experiments demonstrate that by using the estimated population size and sharing distance an evolutionary algorithm (EA) correctly identifies multiple optima.","PeriodicalId":152088,"journal":{"name":"Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114188558","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
A hierarchical evolutionary approach to multi-objective optimization 多目标优化的层次进化方法
Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753) Pub Date : 2004-06-19 DOI: 10.1109/CEC.2004.1331134
C. Mumford
{"title":"A hierarchical evolutionary approach to multi-objective optimization","authors":"C. Mumford","doi":"10.1109/CEC.2004.1331134","DOIUrl":"https://doi.org/10.1109/CEC.2004.1331134","url":null,"abstract":"This work describes a hierarchical evolutionary approach to Pareto-based multi-objective optimization. Using the SEAMO algorithm (a simple evolutionary algorithm for multi-objective optimization) as a basis, it demonstrates how it is possible to obtain a better spread of results if subpopulations of various sizes are used in a simple hierarchical framework. Three alternative hierarchical models are tried and the results compared.","PeriodicalId":152088,"journal":{"name":"Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114902400","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 novel concurrent particle swarm optimization 一种新的并行粒子群优化算法
Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753) Pub Date : 2004-06-19 DOI: 10.1109/CEC.2004.1330940
S. Baskar, P. N. Suganthan
{"title":"A novel concurrent particle swarm optimization","authors":"S. Baskar, P. N. Suganthan","doi":"10.1109/CEC.2004.1330940","DOIUrl":"https://doi.org/10.1109/CEC.2004.1330940","url":null,"abstract":"In this paper, a concurrent PSO (CONPSO) algorithm is proposed to alleviate the premature convergence problem of PSO algorithm. It is a type of parallel algorithm in which modified PSO and FDR-PS algorithms are simulated concurrently with frequent message passing between them. This algorithm avoids the possible crosstalk effect of pbest and gbest terms with nbest term in FDR-PSO. Thereby, search efficiency of proposed algorithm is improved. In order to demonstrate the effectiveness of the proposed algorithm, experiments were conducted on six benchmarks continuous optimization problems. Results clearly demonstrate the superior performance of the proposed algorithm in terms of solution quality, average computation time and consistency. This algorithm is very much suitable for the implementation in parallel computer.","PeriodicalId":152088,"journal":{"name":"Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114734814","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: 109
An investigation on the roles of insertion and deletion operators in tree adjoining grammar guided genetic programming 树相邻语法引导遗传规划中插入和删除操作符作用的研究
Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753) Pub Date : 2004-06-19 DOI: 10.1109/CEC.2004.1330894
N. X. Hoai, Robert Ian Mc Kay
{"title":"An investigation on the roles of insertion and deletion operators in tree adjoining grammar guided genetic programming","authors":"N. X. Hoai, Robert Ian Mc Kay","doi":"10.1109/CEC.2004.1330894","DOIUrl":"https://doi.org/10.1109/CEC.2004.1330894","url":null,"abstract":"We investigate the roles of insertion and deletion as mutation operators and local search operators in a tree adjoining grammar guided genetic programming (TAG3P) system (Nguyen Xuan Hoai et al., 2003). The results show that, on three standard problems, these operators work better as mutation operators than the more standard sub-tree mutation originally used in (Nguyen Xuan Hoai et al., 2003, 2004). Moreover, for some problems, insertion and deletion can act effectively as local search operators, allowing TAG3P to solve problems with very small population sizes.","PeriodicalId":152088,"journal":{"name":"Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125619028","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: 5
A comparison of two mutation operators for the path planning problem 路径规划问题中两种变异算子的比较
Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753) Pub Date : 2004-06-19 DOI: 10.1109/CEC.2004.1330953
S. Esquivel, M. García, G. Leguizamón, Maximiliano Ribba
{"title":"A comparison of two mutation operators for the path planning problem","authors":"S. Esquivel, M. García, G. Leguizamón, Maximiliano Ribba","doi":"10.1109/CEC.2004.1330953","DOIUrl":"https://doi.org/10.1109/CEC.2004.1330953","url":null,"abstract":"This work presents a comparative analysis of two problem-sensitive mutation operators for the off-line path planning problem. Our aim is to study the behavior of an evolutionary algorithm in stationary environments in order to extend it to the on-line path planning problem. The main difference between the two mutation operators studied is that one of them defines a control mechanism for the extent of exploration. The results show that this last operator improves the quality of the paths found by the algorithm.","PeriodicalId":152088,"journal":{"name":"Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125689034","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: 0
Learning to play like a human: case injected genetic algorithms for strategic computer gaming 学习像人一样下棋:为战略电脑游戏注入遗传算法
Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753) Pub Date : 2004-06-19 DOI: 10.1117/12.668273
C. Miles, S. Louis, N. Cole, J. McDonnell
{"title":"Learning to play like a human: case injected genetic algorithms for strategic computer gaming","authors":"C. Miles, S. Louis, N. Cole, J. McDonnell","doi":"10.1117/12.668273","DOIUrl":"https://doi.org/10.1117/12.668273","url":null,"abstract":"We use case injected genetic algorithms to learn how to competently play computer strategy games. Strategic computer games involve long range planning across complex dynamics and imperfect knowledge presented to players requires them to anticipate opponent moves and adapt their strategies accordingly. This work addresses the problem of acquiring and using knowledge from human players for such games. Specifically, we learn general routing information from a human player and use case-injected genetic algorithms to incorporate this acquired knowledge in subsequent planning. Results from a strike planning game show that with an appropriate representation, case injection effectively biases the genetic algorithm toward producing plans that contain important strategic elements used by human players.","PeriodicalId":152088,"journal":{"name":"Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126031618","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: 29
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