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.2002.1004419
M. Minami, Atsushi Tamamura, T. Asakura
Dynamical interferences have been thought that they should be erased to improve control accuracy. However it may be possible to improve the performance of total motion using the interferences. We propose a method to acquire a kind of machine intelligence to utilize dynamically interfered motion. The machine intelligence is defined here as an ability that the machine can find by itself a way to use dynamical interferences and nonlinear friction to obtain a desired motion. We propose a strategy of how a machine uses the effects of the dynamical interferences, and how it acquires the way to achieve an objective motion. The desired motion is traveling of a 1-link mobile manipulator by using interfering motion of the mounted link, which does not possess driving motors nor brakes. The proposed method is composed of functions to give the machine sample motions using Fourier series and to improve the Fourier coefficients by evaluating the motion results based on a function used in a genetic algorithm as a fitness function. Further, an ability to avoid collisions between the mounted manipulator and the floor is added to the traveling ability to confirm that the proposed method could be adapted to many objectives. We confirmed by simulations and real experiments that the mobile manipulator could find effective motion that makes it travel forward without colliding against the floor.
{"title":"Machine intelligence of a mobile manipulator to utilize dynamically interfered motion and nonlinear friction","authors":"M. Minami, Atsushi Tamamura, T. Asakura","doi":"10.1109/CEC.2002.1004419","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004419","url":null,"abstract":"Dynamical interferences have been thought that they should be erased to improve control accuracy. However it may be possible to improve the performance of total motion using the interferences. We propose a method to acquire a kind of machine intelligence to utilize dynamically interfered motion. The machine intelligence is defined here as an ability that the machine can find by itself a way to use dynamical interferences and nonlinear friction to obtain a desired motion. We propose a strategy of how a machine uses the effects of the dynamical interferences, and how it acquires the way to achieve an objective motion. The desired motion is traveling of a 1-link mobile manipulator by using interfering motion of the mounted link, which does not possess driving motors nor brakes. The proposed method is composed of functions to give the machine sample motions using Fourier series and to improve the Fourier coefficients by evaluating the motion results based on a function used in a genetic algorithm as a fitness function. Further, an ability to avoid collisions between the mounted manipulator and the floor is added to the traveling ability to confirm that the proposed method could be adapted to many objectives. We confirmed by simulations and real experiments that the mobile manipulator could find effective motion that makes it travel forward without colliding against the floor.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"1 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":"128216123","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}
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.2002.1006211
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}
Pub Date : 2002-05-12DOI: 10.1109/CEC.2002.1006285
Chumniao Wang, William Y. C. Soh, Han Wang, Hui Wang
In this paper, a new hierarchical genetic algorithm for path planning in a static environment with obstacles is presented. The algorithm of path planning in this paper is inspired by the Dubins' theorem regarding shortest paths of bounded curvature in the absence of obstacles. The algorithm is based on the Dubins' theorem to simplify the problem model, the genetic algorithm to search the best path, a special hierarchical structure of the chromosome to denote a possible path in the environment, the special genetic operators for each module, a penalty strategy to "punish" the infeasible chromosomes during searching. The performance results presented have shown that the approach is able to produce high quality solutions in reasonable time.
{"title":"A hierarchical genetic algorithm for path planning in a static environment with obstacles","authors":"Chumniao Wang, William Y. C. Soh, Han Wang, Hui Wang","doi":"10.1109/CEC.2002.1006285","DOIUrl":"https://doi.org/10.1109/CEC.2002.1006285","url":null,"abstract":"In this paper, a new hierarchical genetic algorithm for path planning in a static environment with obstacles is presented. The algorithm of path planning in this paper is inspired by the Dubins' theorem regarding shortest paths of bounded curvature in the absence of obstacles. The algorithm is based on the Dubins' theorem to simplify the problem model, the genetic algorithm to search the best path, a special hierarchical structure of the chromosome to denote a possible path in the environment, the special genetic operators for each module, a penalty strategy to \"punish\" the infeasible chromosomes during searching. The performance results presented have shown that the approach is able to produce high quality solutions in reasonable time.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"1 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":"132873275","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.2002.1004520
Burcin Aktan, G. Greenwood, M. Shor
Adaptation of the fitness criteria can be a very powerful tool, enhancing the feedback scheme employed in standard evolutionary algorithms. When the problem the evolutionary algorithm (EA) is trying to solve is changing over time, the fitness criteria need to change to adapt to the new problem. Significant performance improvements are possible with feedback based adaptation schemes. This work outlines the results of an adaptation scheme applied to maximization of the functional test coverage problem.
{"title":"Improving evolutionary algorithm performance on maximizing functional test coverage of ASICs using adaptation of the fitness criteria","authors":"Burcin Aktan, G. Greenwood, M. Shor","doi":"10.1109/CEC.2002.1004520","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004520","url":null,"abstract":"Adaptation of the fitness criteria can be a very powerful tool, enhancing the feedback scheme employed in standard evolutionary algorithms. When the problem the evolutionary algorithm (EA) is trying to solve is changing over time, the fitness criteria need to change to adapt to the new problem. Significant performance improvements are possible with feedback based adaptation schemes. This work outlines the results of an adaptation scheme applied to maximization of the functional test coverage problem.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"212 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":"122380286","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.2002.1006270
D. Sofge, K. A. Jong, A. Schultz
Cooperative coevolutionary architectures provide a framework for solving complex problems by decomposing them into constituent subproblems, solving the subproblems, and then reintegrating the solutions. This paper presents a blended cooperative coevolution model which offers advantages over traditional evolutionary algorithms and currently-used cooperative coevolutionary architectures.
{"title":"A blended population approach to cooperative coevolution for decomposition of complex problems","authors":"D. Sofge, K. A. Jong, A. Schultz","doi":"10.1109/CEC.2002.1006270","DOIUrl":"https://doi.org/10.1109/CEC.2002.1006270","url":null,"abstract":"Cooperative coevolutionary architectures provide a framework for solving complex problems by decomposing them into constituent subproblems, solving the subproblems, and then reintegrating the solutions. This paper presents a blended cooperative coevolution model which offers advantages over traditional evolutionary algorithms and currently-used cooperative coevolutionary architectures.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"2015 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":"121321262","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.2002.1006214
Masahito Yamamoto, A. Kameda, N. Matsuura, Toshikazu Shiba, A. Ohuchi
In this paper, the results of analysis of the hybridization process in DNA computing by using a simulation model are presented. The simulation model has some parameters that influence the results of computation. The relations between these parameters and the results of simulations and laboratory experiments are therefore discussed.
{"title":"Simulation analysis of hybridization process for DNA computing with concentration control","authors":"Masahito Yamamoto, A. Kameda, N. Matsuura, Toshikazu Shiba, A. Ohuchi","doi":"10.1109/CEC.2002.1006214","DOIUrl":"https://doi.org/10.1109/CEC.2002.1006214","url":null,"abstract":"In this paper, the results of analysis of the hybridization process in DNA computing by using a simulation model are presented. The simulation model has some parameters that influence the results of computation. The relations between these parameters and the results of simulations and laboratory experiments are therefore discussed.","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":"121459795","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.2002.1004408
B. Julstrom
The rectilinear Steiner problem seeks the shortest tree made up of horizontal and vertical line segments that connects a set of points in the plane. The extra points where the segments meet are called Steiner points. Evolutionary algorithms for this problem have encoded rectilinear Steiner trees by extending codings of spanning trees to specify Steiner point choices for the spanning tree edges. These algorithms have been slow and have performed poorly on larger problem instances. The genetic algorithm presented here searches only the space of Steiner point assignments to the edges of a minimum rectilinear spanning tree. In tests on 45 instances of the rectilinear Steiner problem, it returns good, though never optimal, trees. The algorithm scales well; it evaluates chromosomes in time that is linear in the number of points, and its performance does not deteriorate as that number increases.
{"title":"A scalable genetic algorithm for the rectilinear Steiner problem","authors":"B. Julstrom","doi":"10.1109/CEC.2002.1004408","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004408","url":null,"abstract":"The rectilinear Steiner problem seeks the shortest tree made up of horizontal and vertical line segments that connects a set of points in the plane. The extra points where the segments meet are called Steiner points. Evolutionary algorithms for this problem have encoded rectilinear Steiner trees by extending codings of spanning trees to specify Steiner point choices for the spanning tree edges. These algorithms have been slow and have performed poorly on larger problem instances. The genetic algorithm presented here searches only the space of Steiner point assignments to the edges of a minimum rectilinear spanning tree. In tests on 45 instances of the rectilinear Steiner problem, it returns good, though never optimal, trees. The algorithm scales well; it evaluates chromosomes in time that is linear in the number of points, and its performance does not deteriorate as that number increases.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"34 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":"129002544","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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