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}
Pub Date : 2002-05-12DOI: 10.1109/CEC.2002.1007014
C. L. Valenzuela
A simple steady-state, Pareto-based evolutionary algorithm is presented that uses an elitist strategy for replacement and a simple uniform scheme for selection. Throughout the genetic search, progress depends entirely on the replacement policy, and no fitness calculations, rankings, subpopulations, niches or auxiliary populations are required. Preliminary results presented in this paper show improvements on previously published results for some multiple knapsack problems.
{"title":"A simple evolutionary algorithm for multi-objective optimization (SEAMO)","authors":"C. L. Valenzuela","doi":"10.1109/CEC.2002.1007014","DOIUrl":"https://doi.org/10.1109/CEC.2002.1007014","url":null,"abstract":"A simple steady-state, Pareto-based evolutionary algorithm is presented that uses an elitist strategy for replacement and a simple uniform scheme for selection. Throughout the genetic search, progress depends entirely on the replacement policy, and no fitness calculations, rankings, subpopulations, niches or auxiliary populations are required. Preliminary results presented in this paper show improvements on previously published results for some multiple knapsack problems.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"117 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":"116616778","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.2002.1004546
S. Oeda, T. Icmmura, T. Yamashita
Artificial immune systems have been identified as artificially intelligent systems. Some algorithms have been developed on this antigen-antibody response. Here, a model is presented wherein the behavior of each immune cell is specified. We improve this model using knowledge of the major histocompatibility complex. For this purpose an evolutionary neural network was used. Qualitative analysis of the results offers verification of the effectiveness of this approach to simulating an immune system.
{"title":"Biological immune system by evolutionary adaptive learning of neural networks","authors":"S. Oeda, T. Icmmura, T. Yamashita","doi":"10.1109/CEC.2002.1004546","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004546","url":null,"abstract":"Artificial immune systems have been identified as artificially intelligent systems. Some algorithms have been developed on this antigen-antibody response. Here, a model is presented wherein the behavior of each immune cell is specified. We improve this model using knowledge of the major histocompatibility complex. For this purpose an evolutionary neural network was used. Qualitative analysis of the results offers verification of the effectiveness of this approach to simulating an immune system.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"26 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":"131567781","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.2002.1004419
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.2002.1006283
B. Al-kazemi, C. Mohan
Multi-phase particle swarm optimization is a new algorithm to be used for discrete and continuous problems. In this algorithm, different groups of particles have trajectories that proceed with differing goals in different phases of the algorithm. On several benchmark problems, the algorithm outperforms standard particle swarm optimization, genetic algorithm, and evolution programming.
{"title":"Multi-phase generalization of the particle swarm optimization algorithm","authors":"B. Al-kazemi, C. Mohan","doi":"10.1109/CEC.2002.1006283","DOIUrl":"https://doi.org/10.1109/CEC.2002.1006283","url":null,"abstract":"Multi-phase particle swarm optimization is a new algorithm to be used for discrete and continuous problems. In this algorithm, different groups of particles have trajectories that proceed with differing goals in different phases of the algorithm. On several benchmark problems, the algorithm outperforms standard particle swarm optimization, genetic algorithm, and evolution programming.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"91 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":"131996801","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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