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.2002.1004428
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.2002.1004431
K. Tan, A. Tay, Tong-heng Lee, C. M. Heng
Genetic programming (GP) has emerged as a promising approach to deal with the classification task in data mining. This paper extends the tree representation of GP to evolve multiple comprehensible IF-THEN classification rules. We introduce a concept mapping technique for the fitness evaluation of individuals. A covering algorithm that employs an artificial immune system-like memory vector is utilized to produce multiple rules as well as to remove redundant rules. The proposed GP classifier is validated on nine benchmark data sets, and the simulation results confirm the viability and effectiveness of the GP approach for solving data mining problems in a wide spectrum of application domains.
{"title":"Mining multiple comprehensible classification rules using genetic programming","authors":"K. Tan, A. Tay, Tong-heng Lee, C. M. Heng","doi":"10.1109/CEC.2002.1004431","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004431","url":null,"abstract":"Genetic programming (GP) has emerged as a promising approach to deal with the classification task in data mining. This paper extends the tree representation of GP to evolve multiple comprehensible IF-THEN classification rules. We introduce a concept mapping technique for the fitness evaluation of individuals. A covering algorithm that employs an artificial immune system-like memory vector is utilized to produce multiple rules as well as to remove redundant rules. The proposed GP classifier is validated on nine benchmark data sets, and the simulation results confirm the viability and effectiveness of the GP approach for solving data mining problems in a wide spectrum of application domains.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"98 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":"124910823","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.2002.1004551
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.2002.1004529
Peter R. W. Harvey, D. Booth, J. Boyce
We present a mutable input field concept that allows a neural network to evolve a mapping between its input layer and a 3-dimensional input cube consisting of a local window applied within multiple imagery sources, such as hyperspectral bands, feature maps, or even encoded tactical information regarding likely object location and class. This allows the net to exploit salient regions (both within and across sources) of what may otherwise be an unwieldy input domain. Small recurrent neural networks are evolved to perform object detection within airborne reconnaissance imagery that has been processed to provide 3 colour bands and 2 feature maps including one designed to identify man-made structures based on perpendicularity of edge direction. A variable input field is shown to provide faster convergence and superior detector fitness over a number of trials than a set of alternative fixed input field mappings.
{"title":"Evolving the mapping between input neurons and multi-source imagery","authors":"Peter R. W. Harvey, D. Booth, J. Boyce","doi":"10.1109/CEC.2002.1004529","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004529","url":null,"abstract":"We present a mutable input field concept that allows a neural network to evolve a mapping between its input layer and a 3-dimensional input cube consisting of a local window applied within multiple imagery sources, such as hyperspectral bands, feature maps, or even encoded tactical information regarding likely object location and class. This allows the net to exploit salient regions (both within and across sources) of what may otherwise be an unwieldy input domain. Small recurrent neural networks are evolved to perform object detection within airborne reconnaissance imagery that has been processed to provide 3 colour bands and 2 feature maps including one designed to identify man-made structures based on perpendicularity of edge direction. A variable input field is shown to provide faster convergence and superior detector fitness over a number of trials than a set of alternative fixed input field mappings.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"60 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":"126498212","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.2002.1004476
J. Vesterstrom, J. Riget, T. Krink
We introduce Division of Labor (DoL) from social insects to improve local optimisation of the Particle Swarm Optimiser (PSO). We compared the performance with the basic PSO, a GA and simulated annealing and found improvements around local optima. The PSO with DoL outperforms the basic PSO on most testcases and is comparable in local optimisation with SA.
{"title":"Division of labor in particle swarm optimisation","authors":"J. Vesterstrom, J. Riget, T. Krink","doi":"10.1109/CEC.2002.1004476","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004476","url":null,"abstract":"We introduce Division of Labor (DoL) from social insects to improve local optimisation of the Particle Swarm Optimiser (PSO). We compared the performance with the basic PSO, a GA and simulated annealing and found improvements around local optima. The PSO with DoL outperforms the basic PSO on most testcases and is comparable in local optimisation with SA.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"120 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":"124737227","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.2002.1004454
Suiliong Liang, A. N. Zincir-Heywood, M. Heywood
Although adaptive and heuristic approaches perform well under idealized conditions to the packet network routing problem, such algorithms are also dependent on global information that is not available under real-world conditions. This work benchmarks routing under local information conditions using the AntNet algorithm and makes recommendations regarding future approaches.
{"title":"The effect of routing under local information using a social insect metaphor","authors":"Suiliong Liang, A. N. Zincir-Heywood, M. Heywood","doi":"10.1109/CEC.2002.1004454","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004454","url":null,"abstract":"Although adaptive and heuristic approaches perform well under idealized conditions to the packet network routing problem, such algorithms are also dependent on global information that is not available under real-world conditions. This work benchmarks routing under local information conditions using the AntNet algorithm and makes recommendations regarding future approaches.","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":"131114156","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.2002.1006211
V. Ciesielski, D. Mawhinney
We have investigated an approach to preventing or minimising the occurrence of premature convergence by measuring the similarity between the programs in the population and replacing the most similar ones with randomly generated programs. On a problem with known premature convergence behaviour, the MAX problem, similarity replacement significantly decreased the rate of premature convergence over the best that could be achieved by manipulation of the mutation rate. The expected CPU time for a successful run was increased due to the additional cost of the similarity matching. On a problem which has a very expensive fitness function, the evolution of a team of soccer playing programs, the degree of premature convergence rate was also significantly reduced. However, in this case the expected time for a successful run was significantly decreased indicating that similarity replacement can be worthwhile for problems with expensive evaluation functions. A significant discovery from our experimental work is that a small change to the way mutation is carried out can result in significant reductions in premature convergence.
{"title":"Prevention of early convergence in genetic programming by replacement of similar programs","authors":"V. Ciesielski, D. Mawhinney","doi":"10.1109/CEC.2002.1006211","DOIUrl":"https://doi.org/10.1109/CEC.2002.1006211","url":null,"abstract":"We have investigated an approach to preventing or minimising the occurrence of premature convergence by measuring the similarity between the programs in the population and replacing the most similar ones with randomly generated programs. On a problem with known premature convergence behaviour, the MAX problem, similarity replacement significantly decreased the rate of premature convergence over the best that could be achieved by manipulation of the mutation rate. The expected CPU time for a successful run was increased due to the additional cost of the similarity matching. On a problem which has a very expensive fitness function, the evolution of a team of soccer playing programs, the degree of premature convergence rate was also significantly reduced. However, in this case the expected time for a successful run was significantly decreased indicating that similarity replacement can be worthwhile for problems with expensive evaluation functions. A significant discovery from our experimental work is that a small change to the way mutation is carried out can result in significant reductions in premature convergence.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"34 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131470752","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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