Pub Date : 2002-05-12DOI: 10.1109/CEC.2002.1004488
J. Khan, S. M. Sait, M. Minhas
In each iteration of a simulated evolution (SE) algorithm for VLSI placement, poorly placed cells are selected probabilistically, based on a measure known as 'goodness'. To compensate for the error in the goodness calculation (and to maintain the number of selected cells within some limit), a parameter known as 'bias' is used, which has major impact on the algorithm's run-time and on the quality of the solution subspace searched. However, it is difficult to select the appropriate value of this selection bias because it varies for each problem instance. In this paper, a biasless selection scheme for the SE algorithm is proposed. This scheme eliminates the human interaction needed in the selection of the bias value for each problem instance. Due to the imprecise nature of the design information at the placement stage, fuzzy logic is used in all stages of the SE algorithm. The proposed scheme was compared with an adaptive bias scheme and was always able to achieve better solutions.
{"title":"Fuzzy biasless simulated evolution for multiobjective VLSI placement","authors":"J. Khan, S. M. Sait, M. Minhas","doi":"10.1109/CEC.2002.1004488","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004488","url":null,"abstract":"In each iteration of a simulated evolution (SE) algorithm for VLSI placement, poorly placed cells are selected probabilistically, based on a measure known as 'goodness'. To compensate for the error in the goodness calculation (and to maintain the number of selected cells within some limit), a parameter known as 'bias' is used, which has major impact on the algorithm's run-time and on the quality of the solution subspace searched. However, it is difficult to select the appropriate value of this selection bias because it varies for each problem instance. In this paper, a biasless selection scheme for the SE algorithm is proposed. This scheme eliminates the human interaction needed in the selection of the bias value for each problem instance. Due to the imprecise nature of the design information at the placement stage, fuzzy logic is used in all stages of the SE algorithm. The proposed scheme was compared with an adaptive bias scheme and was always able to achieve better solutions.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129886049","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}
Pub Date : 2002-05-12DOI: 10.1109/CEC.2002.1007061
Jiangning Xu, T. Arslan, Dejun Wan, Qing Wang
In this paper, a new technique that uses a specially tailored genetic algorithm is proposed for attitude determination via GPS carrier phase observables. The technique overcomes restrictions due to computational overheads incurred by existing techniques such as the ambiguity function method. We present experimental results which show that the algorithm is able to efficiently search the complex search space imposed by the problem in addition to being immune to cycle slips compared to other conventional methods.
{"title":"GPS attitude determination using a genetic algorithm","authors":"Jiangning Xu, T. Arslan, Dejun Wan, Qing Wang","doi":"10.1109/CEC.2002.1007061","DOIUrl":"https://doi.org/10.1109/CEC.2002.1007061","url":null,"abstract":"In this paper, a new technique that uses a specially tailored genetic algorithm is proposed for attitude determination via GPS carrier phase observables. The technique overcomes restrictions due to computational overheads incurred by existing techniques such as the ambiguity function method. We present experimental results which show that the algorithm is able to efficiently search the complex search space imposed by the problem in addition to being immune to cycle slips compared to other conventional methods.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128874888","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}
Pub Date : 2002-05-12DOI: 10.1109/CEC.2002.1004433
M. Wong, Shing Yan Lee, K. Leung
A novel hybrid framework is reported that improves upon our previous work, MDLEP, which uses evolutionary programming to solve the difficult Bayesian network learning problem. A new merge operator is also introduced that further enhances the efficiency. As experimental results suggest, our hybrid approach performs significantly better than MDLEP.
{"title":"A hybrid approach to learn Bayesian networks using evolutionary programming","authors":"M. Wong, Shing Yan Lee, K. Leung","doi":"10.1109/CEC.2002.1004433","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004433","url":null,"abstract":"A novel hybrid framework is reported that improves upon our previous work, MDLEP, which uses evolutionary programming to solve the difficult Bayesian network learning problem. A new merge operator is also introduced that further enhances the efficiency. As experimental results suggest, our hybrid approach performs significantly better than MDLEP.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"63 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121005529","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}
Pub Date : 2002-05-12DOI: 10.1109/CEC.2002.1004552
T. Hamada, H. Kawamura, Masahito Yamamoto, A. Ohuchi
We analyze the behavior of players in the situation where they recognize an identical situation as a simple market-like place differently. In our model a player considers the others as a representative player. We examine the player's behavior when with and without fluidity of players.
{"title":"A study on behavioral structure of artificial market based on adaptive game","authors":"T. Hamada, H. Kawamura, Masahito Yamamoto, A. Ohuchi","doi":"10.1109/CEC.2002.1004552","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004552","url":null,"abstract":"We analyze the behavior of players in the situation where they recognize an identical situation as a simple market-like place differently. In our model a player considers the others as a representative player. We examine the player's behavior when with and without fluidity of players.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124047350","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}
Pub Date : 2002-05-12DOI: 10.1109/CEC.2002.1006258
C. Cambier, M. Piron, A. Cardon
We deal with systems using massive multi-agent organizations and expressing complex problems like the representation of the world sub-system managing the behavior of a robot. We propose an analysis and an operating representation of multi-agent organization in a geometric way, using specific multi-agent organization in a morphologic agent space. We propose also an architecture expressing the behavior of the massive multi-agent organization. So we open the way to the implementation of self-adaptive systems. We present an application for the behavior of an autonomous robot.
{"title":"Self-adaptive systems using a massive multi-agent system","authors":"C. Cambier, M. Piron, A. Cardon","doi":"10.1109/CEC.2002.1006258","DOIUrl":"https://doi.org/10.1109/CEC.2002.1006258","url":null,"abstract":"We deal with systems using massive multi-agent organizations and expressing complex problems like the representation of the world sub-system managing the behavior of a robot. We propose an analysis and an operating representation of multi-agent organization in a geometric way, using specific multi-agent organization in a morphologic agent space. We propose also an architecture expressing the behavior of the massive multi-agent organization. So we open the way to the implementation of self-adaptive systems. We present an application for the behavior of an autonomous robot.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124405641","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}
Pub Date : 2002-05-12DOI: 10.1109/CEC.2002.1006262
V. Cutello, E. Mastriani, F. Pappalardo
We propose an evolutionary algorithm to approximate optimal solutions to instances of the T-constrained variation of the Minimum Hitting Set Problem. The base problem, Minimum Hitting Set, is a well known /spl Nscr//spl Pscr/-complete problem. Our genetic algorithm will use the idea of viruses which infect chromosomes and change one of their bits. A special dynamic fitness function has been also used to improve overall performance.
{"title":"An evolutionary algorithm for the T-constrained variation of Minimum Hitting Set problem","authors":"V. Cutello, E. Mastriani, F. Pappalardo","doi":"10.1109/CEC.2002.1006262","DOIUrl":"https://doi.org/10.1109/CEC.2002.1006262","url":null,"abstract":"We propose an evolutionary algorithm to approximate optimal solutions to instances of the T-constrained variation of the Minimum Hitting Set Problem. The base problem, Minimum Hitting Set, is a well known /spl Nscr//spl Pscr/-complete problem. Our genetic algorithm will use the idea of viruses which infect chromosomes and change one of their bits. A special dynamic fitness function has been also used to improve overall performance.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124433487","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}
Evolutionary programming has been applied to many optimization problems. However, on some function optimization problems its convergence rate is slow. In this paper, swarm directions are embedded in fast evolutionary programming. The swarm direction for an individual supplies its place to be mutated. The experimental results show its effectiveness and efficiency.
{"title":"Swarm directions embedded in fast evolutionary programming","authors":"Chengjian Wei, Zhenya He, Yifeng Zhang, Wenjiang Pei","doi":"10.1109/CEC.2002.1004427","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004427","url":null,"abstract":"Evolutionary programming has been applied to many optimization problems. However, on some function optimization problems its convergence rate is slow. In this paper, swarm directions are embedded in fast evolutionary programming. The swarm direction for an individual supplies its place to be mutated. The experimental results show its effectiveness and efficiency.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132423279","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}
Pub Date : 2002-05-12DOI: 10.1109/CEC.2002.1004479
Morten Løvbjerg, T. Krink
Particle swarm optimisers (PSOs) show potential in function optimisation, but still have room for improvement. Self-organized criticality (SOC) can help control the PSO and add diversity. Extending the PSO with SOC seems promising reaching faster convergence and better solutions.
{"title":"Extending particle swarm optimisers with self-organized criticality","authors":"Morten Løvbjerg, T. Krink","doi":"10.1109/CEC.2002.1004479","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004479","url":null,"abstract":"Particle swarm optimisers (PSOs) show potential in function optimisation, but still have room for improvement. Self-organized criticality (SOC) can help control the PSO and add diversity. Extending the PSO with SOC seems promising reaching faster convergence and better solutions.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133731373","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}
Pub Date : 2002-05-12DOI: 10.1109/CEC.2002.1004456
J. Isaacs, Robert K. Watkins, S. Foo
Using a genetic algorithm (GA) to evolve ant colony systems (ACS), we have succeeded at producing evolvable random number generators (RNG) that can be written to hardware. Although the simulated behavior of individual ants is limited to a small number of choices, "fit" colonies pass many stringent tests for randomness.
{"title":"Evolving ant colony systems in hardware for random number generation","authors":"J. Isaacs, Robert K. Watkins, S. Foo","doi":"10.1109/CEC.2002.1004456","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004456","url":null,"abstract":"Using a genetic algorithm (GA) to evolve ant colony systems (ACS), we have succeeded at producing evolvable random number generators (RNG) that can be written to hardware. Although the simulated behavior of individual ants is limited to a small number of choices, \"fit\" colonies pass many stringent tests for randomness.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133782058","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}
Pub Date : 2002-05-12DOI: 10.1109/CEC.2002.1004484
Kevin P. Anchor, G. Lamont, G. Gunsch
Attacks against computer networks are becoming more sophisticated, with adversaries using new attacks or modifying exiting attacks. This research presents an initial step in using an evolutionary programming approach to develop a system for automatically detecting attacks with features similar to known attacks. Initial testing shows the algorithm performs satisfactorily.
{"title":"An evolutionary programming approach for detecting novel computer network attacks","authors":"Kevin P. Anchor, G. Lamont, G. Gunsch","doi":"10.1109/CEC.2002.1004484","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004484","url":null,"abstract":"Attacks against computer networks are becoming more sophisticated, with adversaries using new attacks or modifying exiting attacks. This research presents an initial step in using an evolutionary programming approach to develop a system for automatically detecting attacks with features similar to known attacks. Initial testing shows the algorithm performs satisfactorily.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122208275","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}
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