Several specific methods have been proposed for handling nonlinear constraints. These methods have to bring individuals in the feasible space, and help to explore and exploit efficiently the feasible domain. However, even if this domain is not sparse, this paper demonstrates that the exploration capacity of standard reproduction operators is not optimal when solving constrained problems. The logarithmic mutation operator presented in this paper has been conceived to explore both locally and globally the search space. As expected, it exhibits a robust and efficient behavior on a constrained version of the Sphere problem, compared to some other standard operators. Associated with BLX-0.5 crossover and a special ranking selection taking the constraints into account, the logarithmic mutation allows a GA to often reach better performance than several well known methods on a set of classical test cases.
{"title":"The need for improving the exploration operators for constrained optimization problems","authors":"S. B. Hamida, A. Pétrowski","doi":"10.1109/CEC.2000.870781","DOIUrl":"https://doi.org/10.1109/CEC.2000.870781","url":null,"abstract":"Several specific methods have been proposed for handling nonlinear constraints. These methods have to bring individuals in the feasible space, and help to explore and exploit efficiently the feasible domain. However, even if this domain is not sparse, this paper demonstrates that the exploration capacity of standard reproduction operators is not optimal when solving constrained problems. The logarithmic mutation operator presented in this paper has been conceived to explore both locally and globally the search space. As expected, it exhibits a robust and efficient behavior on a constrained version of the Sphere problem, compared to some other standard operators. Associated with BLX-0.5 crossover and a special ranking selection taking the constraints into account, the logarithmic mutation allows a GA to often reach better performance than several well known methods on a set of classical test cases.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"296 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115425881","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}
In this paper a novel approach to performing classification is presented, hypersurface discriminant functions are evolved using genetic programming. These discriminant functions reside in the states of finite state automata which have the ability to reason and logically combine the hypersurfaces to generate a complex decision space. An object may be classified by one or many of the discriminant functions, this is decided by the automata. During the evolution of this symbiotic architecture, feature selection for each of the discriminant functions is achieved implicitly, a task which is normally performed before a classification algorithm is trained. Since each discriminant function has different features, and objects may be classified with one or more discriminant functions, no two objects from the same class need be classified using the same features. Instead, the most appropriate features for a given object are used.
{"title":"Performing classification with an environment manipulating mutable automata (EMMA)","authors":"K. Benson","doi":"10.1109/CEC.2000.870305","DOIUrl":"https://doi.org/10.1109/CEC.2000.870305","url":null,"abstract":"In this paper a novel approach to performing classification is presented, hypersurface discriminant functions are evolved using genetic programming. These discriminant functions reside in the states of finite state automata which have the ability to reason and logically combine the hypersurfaces to generate a complex decision space. An object may be classified by one or many of the discriminant functions, this is decided by the automata. During the evolution of this symbiotic architecture, feature selection for each of the discriminant functions is achieved implicitly, a task which is normally performed before a classification algorithm is trained. Since each discriminant function has different features, and objects may be classified with one or more discriminant functions, no two objects from the same class need be classified using the same features. Instead, the most appropriate features for a given object are used.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121703915","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}
This paper proposes a framework for evolutionary systems to mine implicit knowledge in large scale databases. The idea here is to construct knowledge-based evolutionary systems that apply the power of evolution computation to facilitate the data mining processes. This framework provides the possibility of making two processes, the data mining process and the optimization process, work simultaneously and reciprocally. Based on Cultural Algorithms, the data mining process is supported by symbolic reasoning in the belief space, and the optimization process is supported by evolutionary search in the population space. The evolutionary search in databases can facilitate the data mining process, while the data mining process can also provide knowledge to expedite the search in databases i.e. the data mining process and the evolutionary search can be integrated and benefit from each other. This new approach was applied to a large-scale temporal-spatial database, and the results indicate that it successfully mined out some very interesting patterns that are unknown before. Another advantage of this approach is that it doesn't have to access all information in the database in order to identify some interesting patterns, by automatically "select" useful cases from a large database to avoid the exhaustive search to every cases. This suggests a great potential to reach the goal of efficiency and effectiveness for data mining.
{"title":"Mining knowledge in large scale databases using cultural algorithms with constraint handling mechanisms","authors":"Xidong Jin, R. Reynolds","doi":"10.1109/CEC.2000.870831","DOIUrl":"https://doi.org/10.1109/CEC.2000.870831","url":null,"abstract":"This paper proposes a framework for evolutionary systems to mine implicit knowledge in large scale databases. The idea here is to construct knowledge-based evolutionary systems that apply the power of evolution computation to facilitate the data mining processes. This framework provides the possibility of making two processes, the data mining process and the optimization process, work simultaneously and reciprocally. Based on Cultural Algorithms, the data mining process is supported by symbolic reasoning in the belief space, and the optimization process is supported by evolutionary search in the population space. The evolutionary search in databases can facilitate the data mining process, while the data mining process can also provide knowledge to expedite the search in databases i.e. the data mining process and the evolutionary search can be integrated and benefit from each other. This new approach was applied to a large-scale temporal-spatial database, and the results indicate that it successfully mined out some very interesting patterns that are unknown before. Another advantage of this approach is that it doesn't have to access all information in the database in order to identify some interesting patterns, by automatically \"select\" useful cases from a large database to avoid the exhaustive search to every cases. This suggests a great potential to reach the goal of efficiency and effectiveness for data mining.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126443254","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 computation is a generic name given to the resolution of computational problems that are planned and implemented based on models of the evolutionary process. Most of the evolutionary algorithms that have been proposed follow biological paradigms and the concepts of natural selection, mutation and reproduction. There are, however, other paradigms which may be adopted in the creation of evolutionary algorithms. Several problems involving unstructured environments may be addressed from the point of view of cultural paradigms, which offer plenty of categories of models where one does not know all possible solutions to a problem - a very common situation in real life. This work applies the computational properties of cultural technology to the solution of a specific problem, adapted from the robotics literature. A test environment denoted the "Cultural Algorithms Simulator" was developed to allow anyone to learn more about the rather unconventional characteristics of a cultural technology.
{"title":"Cultural algorithms: concepts and experiments","authors":"B. Franklin, M. Bergerman","doi":"10.1109/CEC.2000.870793","DOIUrl":"https://doi.org/10.1109/CEC.2000.870793","url":null,"abstract":"Evolutionary computation is a generic name given to the resolution of computational problems that are planned and implemented based on models of the evolutionary process. Most of the evolutionary algorithms that have been proposed follow biological paradigms and the concepts of natural selection, mutation and reproduction. There are, however, other paradigms which may be adopted in the creation of evolutionary algorithms. Several problems involving unstructured environments may be addressed from the point of view of cultural paradigms, which offer plenty of categories of models where one does not know all possible solutions to a problem - a very common situation in real life. This work applies the computational properties of cultural technology to the solution of a specific problem, adapted from the robotics literature. A test environment denoted the \"Cultural Algorithms Simulator\" was developed to allow anyone to learn more about the rather unconventional characteristics of a cultural technology.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129965456","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}
B. Paechter, T. Back, Marc Schoenauer, M. Sebag, A. Eiben, J. Merelo, T. Fogarty
This paper describes a project funded by the European Commission which seeks to provide the technology and software infrastructure necessary to support the next generation of evolving infohabitants in a way that makes that infrastructure universal, open and scalable. The Distributed Resource Evolutionary Algorithm Machine (DREAM) will use existing hardware infrastructure in a more efficient manner, by utilising otherwise unused CPU time. It will allow infohabitants to co-operate, communicate, negotiate and trade; and emergent behaviour is expected to result. It is expected that there will be an emergent economy that results from the provision and use of CPU cycles by infohabitants and their owners. The DREAM infrastructure will be evaluated with new work on distributed data mining, distributed scheduling and the modelling of economic and social behaviour.
{"title":"A Distributed Resource Evolutionary Algorithm Machine (DREAM)","authors":"B. Paechter, T. Back, Marc Schoenauer, M. Sebag, A. Eiben, J. Merelo, T. Fogarty","doi":"10.1109/CEC.2000.870746","DOIUrl":"https://doi.org/10.1109/CEC.2000.870746","url":null,"abstract":"This paper describes a project funded by the European Commission which seeks to provide the technology and software infrastructure necessary to support the next generation of evolving infohabitants in a way that makes that infrastructure universal, open and scalable. The Distributed Resource Evolutionary Algorithm Machine (DREAM) will use existing hardware infrastructure in a more efficient manner, by utilising otherwise unused CPU time. It will allow infohabitants to co-operate, communicate, negotiate and trade; and emergent behaviour is expected to result. It is expected that there will be an emergent economy that results from the provision and use of CPU cycles by infohabitants and their owners. The DREAM infrastructure will be evaluated with new work on distributed data mining, distributed scheduling and the modelling of economic and social behaviour.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128505029","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}
Lishan Kang, Zhuo Kang, Yan Li, Pu Liu, Yuping Chen
Recently Tao Guo (1999) proposed a stochastic search algorithm in his PhD thesis for solving function optimization problems. He combined the subspace search method (a general multi-parent recombination strategy) with the population hill-climbing method. The former keeps a global search for the overall situation, and the latter maintains the convergence of the algorithm. Guo's algorithm has many advantages, such as the simplicity of its structure, the high accuracy of its results, the wide range of its applications, and the robustness of its use. In this paper a preliminary theoretical analysis of the algorithm is given and some numerical experiments are performed using Guo's algorithm to demonstrate the theoretical results. Three asynchronous parallel algorithms with different granularities for MIMD machines are designed by parallelizing Guo's algorithm.
{"title":"Asynchronous parallelization of Guo's algorithm for function optimization","authors":"Lishan Kang, Zhuo Kang, Yan Li, Pu Liu, Yuping Chen","doi":"10.1109/CEC.2000.870378","DOIUrl":"https://doi.org/10.1109/CEC.2000.870378","url":null,"abstract":"Recently Tao Guo (1999) proposed a stochastic search algorithm in his PhD thesis for solving function optimization problems. He combined the subspace search method (a general multi-parent recombination strategy) with the population hill-climbing method. The former keeps a global search for the overall situation, and the latter maintains the convergence of the algorithm. Guo's algorithm has many advantages, such as the simplicity of its structure, the high accuracy of its results, the wide range of its applications, and the robustness of its use. In this paper a preliminary theoretical analysis of the algorithm is given and some numerical experiments are performed using Guo's algorithm to demonstrate the theoretical results. Three asynchronous parallel algorithms with different granularities for MIMD machines are designed by parallelizing Guo's algorithm.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130049262","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}
A local navigation algorithm for mobile robots is proposed, based on the new extended virtual force field (EVFF) concept, neural network-based fusion for the three primitive behaviors generated by the EVFF, and the evolutionary programming-based optimization of the neural network weights. Furthermore, a multi-network version of the above neurally-combined EVFF has been proposed that lends itself not only to an efficient architecture but also to a greatly enhanced generalization capability. These techniques have been verified through both simulation and real experiments under a collection of complex environments.
{"title":"An extended virtual force field based behavioral fusion with neural networks and evolutionary programming for mobile robot navigation","authors":"K. Im, Se-Young Oh","doi":"10.1109/CEC.2000.870792","DOIUrl":"https://doi.org/10.1109/CEC.2000.870792","url":null,"abstract":"A local navigation algorithm for mobile robots is proposed, based on the new extended virtual force field (EVFF) concept, neural network-based fusion for the three primitive behaviors generated by the EVFF, and the evolutionary programming-based optimization of the neural network weights. Furthermore, a multi-network version of the above neurally-combined EVFF has been proposed that lends itself not only to an efficient architecture but also to a greatly enhanced generalization capability. These techniques have been verified through both simulation and real experiments under a collection of complex environments.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121832923","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}
The population-based incremental learning (PBIL) algorithm is extended to a form where multiple traits for each gene reflect the pleiotropic and polygenic characteristics in natural evolved systems. This method is used to solve the traveling salesman problem. Some results are better than the best existing algorithms for evolutionary computation of the problem. The results show that the method proposed is comparable to the advanced level of solvers for the traveling salesman problem.
{"title":"An extensive PBIL algorithm with multiple traits and its application","authors":"Zhenya He, Chengjian Wei, Yifeng Zhang, Luxi Yang","doi":"10.1109/CEC.2000.870377","DOIUrl":"https://doi.org/10.1109/CEC.2000.870377","url":null,"abstract":"The population-based incremental learning (PBIL) algorithm is extended to a form where multiple traits for each gene reflect the pleiotropic and polygenic characteristics in natural evolved systems. This method is used to solve the traveling salesman problem. Some results are better than the best existing algorithms for evolutionary computation of the problem. The results show that the method proposed is comparable to the advanced level of solvers for the traveling salesman problem.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121280679","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}
The wide use of field bus based distributed systems in embedded control applications triggered the research on the problem of transmission network induced jitter in control variables. In this paper we introduce a variant of the classical genetic algorithm, which we call progressive genetic algorithm, and show how it can be used to reduce jitter suffered by periodic messages. The approach can be applied either in centrally controlled field buses or in synchronized ones. The algorithm was tested with two well-known and widely used benchmarks: the PSA, coming from automotive industries and the SAE from automatic guided vehicles. It is shown that it is possible to completely eliminate jitter if the adequate transmission rate is available and, if not, a satisfactory reduced jitter can be obtained.
{"title":"Jitter reduction in a real-time message transmission system using genetic algorithms","authors":"J. Barreiros, E. Costa, J. Fonseca, F. Coutinho","doi":"10.1109/CEC.2000.870769","DOIUrl":"https://doi.org/10.1109/CEC.2000.870769","url":null,"abstract":"The wide use of field bus based distributed systems in embedded control applications triggered the research on the problem of transmission network induced jitter in control variables. In this paper we introduce a variant of the classical genetic algorithm, which we call progressive genetic algorithm, and show how it can be used to reduce jitter suffered by periodic messages. The approach can be applied either in centrally controlled field buses or in synchronized ones. The algorithm was tested with two well-known and widely used benchmarks: the PSA, coming from automotive industries and the SAE from automatic guided vehicles. It is shown that it is possible to completely eliminate jitter if the adequate transmission rate is available and, if not, a satisfactory reduced jitter can be obtained.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116422960","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}
This paper presents a model comprising Finite State Machines (FSMs) with embedded Genetic Programs (GPs) which co-evolve to perform the task of Automatic Target Detection (ATD). The fusion of an FSM and GPs allows for a control structure (main program), the FSM, and sub-programs, the GPs, to co-evolve in a symbiotic relationship. The GP outputs along with the FSM state transition levels are used to construct confidence intervals that enable each pixel within the image to be classified as either target or non-target, or to cause a state transition to take place and further analysis of the pixel to be performed. The algorithms produced using this method consist of nominally four GPs, with a typical node cardinality of less than ten, that are executed in an order dictated by the FSM. The results of the experimentation performed are compared to those obtained in two independent studies of the same problem using Kohonen neural networks and a two stage genetic programming strategy.
{"title":"Evolving finite state machines with embedded genetic programming for automatic target detection","authors":"K. Benson","doi":"10.1109/CEC.2000.870838","DOIUrl":"https://doi.org/10.1109/CEC.2000.870838","url":null,"abstract":"This paper presents a model comprising Finite State Machines (FSMs) with embedded Genetic Programs (GPs) which co-evolve to perform the task of Automatic Target Detection (ATD). The fusion of an FSM and GPs allows for a control structure (main program), the FSM, and sub-programs, the GPs, to co-evolve in a symbiotic relationship. The GP outputs along with the FSM state transition levels are used to construct confidence intervals that enable each pixel within the image to be classified as either target or non-target, or to cause a state transition to take place and further analysis of the pixel to be performed. The algorithms produced using this method consist of nominally four GPs, with a typical node cardinality of less than ten, that are executed in an order dictated by the FSM. The results of the experimentation performed are compared to those obtained in two independent studies of the same problem using Kohonen neural networks and a two stage genetic programming strategy.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126405117","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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