Pub Date : 2002-05-12DOI: 10.1109/CEC.2002.1004551
Nils Svangård, P. Nordin, Stefan Lloyd, C. Wihlborg
We have used a linear Genetic Programming system with a multitude of different quotes on financial securities as input in order to evolve an intraday trading strategy for an individual stock, attempting to outperform a simple buy and hold strategy over the same period of time.
{"title":"Evolving short-term trading strategies using genetic programming","authors":"Nils Svangård, P. Nordin, Stefan Lloyd, C. Wihlborg","doi":"10.1109/CEC.2002.1004551","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004551","url":null,"abstract":"We have used a linear Genetic Programming system with a multitude of different quotes on financial securities as input in order to evolve an intraday trading strategy for an individual stock, attempting to outperform a simple buy and hold strategy over the same period of time.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"96 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":"123463775","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.2002.1007031
C. Coello, N. C. Cortés
We present a parallel version of a constraint-handling technique based on the artificial immune system. The proposed approach does not require penalty factors of any kind, it is relatively simple to implement and it is quite competitive with more sophisticated techniques. Additionally, when parallelized using an island scheme, the approach not only reduces its computational time, but it also improves the quality of the results produced.
{"title":"A parallel implementation of an artificial immune system to handle constraints in genetic algorithms: preliminary results","authors":"C. Coello, N. C. Cortés","doi":"10.1109/CEC.2002.1007031","DOIUrl":"https://doi.org/10.1109/CEC.2002.1007031","url":null,"abstract":"We present a parallel version of a constraint-handling technique based on the artificial immune system. The proposed approach does not require penalty factors of any kind, it is relatively simple to implement and it is quite competitive with more sophisticated techniques. Additionally, when parallelized using an island scheme, the approach not only reduces its computational time, but it also improves the quality of the results produced.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"40 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":"123854180","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.2002.1004428
K. Tsui, Jiming Liu
A new population-based stochastic search algorithm called evolutionary diffusion optimization (EDO) inspired by diffusion in nature has been proposed. This article compares the performance of EDO with simulated annealing and fast evolutionary programming. Experimental results show that EDO performs better than SA and FEP in some cases.
{"title":"Evolutionary diffusion optimization. II. Performance assessment","authors":"K. Tsui, Jiming Liu","doi":"10.1109/CEC.2002.1004428","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004428","url":null,"abstract":"A new population-based stochastic search algorithm called evolutionary diffusion optimization (EDO) inspired by diffusion in nature has been proposed. This article compares the performance of EDO with simulated annealing and fast evolutionary programming. Experimental results show that EDO performs better than SA and FEP in some cases.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"2 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":"123316816","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.2002.1007007
Dong Hwa Kim
This paper considers that auto tuning of a 2-DOF PID controller can be effectively performed by immune algorithms. A number of tuning approaches for PID controllers are considered in the context of intelligent tuning methods. However, in the case of a 2-DOF PID controller, tuning based on classical approaches such a trial and error has been suggested. A general view is also provided that they are the special cases of either the linear model or the single control system. On the other hand, since the immune network system possesses a self organizing and distributed memory, it is thus adaptive to its external environment and allows a PDP (parallel distributed processing) network to complete patterns against the environmental situation. It can also provide an optimal solution. Simulation results reveal that immune algorithm based tuning is an effective approach to search for optimal or near optimal control.
{"title":"Tuning of 2-DOF PID controller by immune algorithm","authors":"Dong Hwa Kim","doi":"10.1109/CEC.2002.1007007","DOIUrl":"https://doi.org/10.1109/CEC.2002.1007007","url":null,"abstract":"This paper considers that auto tuning of a 2-DOF PID controller can be effectively performed by immune algorithms. A number of tuning approaches for PID controllers are considered in the context of intelligent tuning methods. However, in the case of a 2-DOF PID controller, tuning based on classical approaches such a trial and error has been suggested. A general view is also provided that they are the special cases of either the linear model or the single control system. On the other hand, since the immune network system possesses a self organizing and distributed memory, it is thus adaptive to its external environment and allows a PDP (parallel distributed processing) network to complete patterns against the environmental situation. It can also provide an optimal solution. Simulation results reveal that immune algorithm based tuning is an effective approach to search for optimal or near optimal control.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"38 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":"115858176","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.2002.1007061
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.2002.1004480
David Harris, S. Bullock
Game-theoretic models provide a rigorous mathematical modelling framework, but tractability considerations keep them simple. In contrast, Evolutionary Simulation Models (ESMs) may be complex, but can lack rigour. We demonstrate that careful synthesis of the two techniques provides improved insights into the processes underlying the evolution of cooperative signalling systems.
{"title":"Enhancing game theory with coevolutionary simulation models of honest signalling","authors":"David Harris, S. Bullock","doi":"10.1109/CEC.2002.1004480","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004480","url":null,"abstract":"Game-theoretic models provide a rigorous mathematical modelling framework, but tractability considerations keep them simple. In contrast, Evolutionary Simulation Models (ESMs) may be complex, but can lack rigour. We demonstrate that careful synthesis of the two techniques provides improved insights into the processes underlying the evolution of cooperative signalling systems.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"2 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":"129555076","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.2002.1004488
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.2002.1004449
J. E. Davis, G. Kendall
Awari is a two-player game of perfect information, played using 12 "pits" and 48 seeds or stones. The aim is for one player to capture more than half the seeds. In this work we show how an awari player can be evolved using a co-evolutionary approach where computer players play against one another, with the strongest players surviving and being mutated using an evolutionary strategy (ES). The players are represented using a simple evaluation function, representing the current game state, with each term of the function having a weight which is evolved using the ES. The output of the evaluation function is used in a mini-max search. We play the best evolved player against one of the strongest shareware programs (Awale) and are able to defeat the program at three of its four levels of play.
{"title":"An investigation, using co-evolution, to evolve an Awari player","authors":"J. E. Davis, G. Kendall","doi":"10.1109/CEC.2002.1004449","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004449","url":null,"abstract":"Awari is a two-player game of perfect information, played using 12 \"pits\" and 48 seeds or stones. The aim is for one player to capture more than half the seeds. In this work we show how an awari player can be evolved using a co-evolutionary approach where computer players play against one another, with the strongest players surviving and being mutated using an evolutionary strategy (ES). The players are represented using a simple evaluation function, representing the current game state, with each term of the function having a weight which is evolved using the ES. The output of the evaluation function is used in a mini-max search. We play the best evolved player against one of the strongest shareware programs (Awale) and are able to defeat the program at three of its four levels of play.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"11 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":"130466564","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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