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}
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}
The NK model introduced by Kauffman (1993) has been widely accepted as a formal model of rugged fitness landscapes. It is shown that the NK model is incapable of accurately modeling an important class of combinatorial optimization problems. Most notable is the limitation in modeling the epistatic relationships that exist in many real-world constrained optimization problems. In addition to introducing a new method of graphically depicting all high dimension fitness landscapes, an extension to the NK model is proposed.
{"title":"Modeling epistatic interactions in fitness landscapes","authors":"Xiaobo Hu, G. Greenwood, S. Ravichandran","doi":"10.1109/CEC.2000.870743","DOIUrl":"https://doi.org/10.1109/CEC.2000.870743","url":null,"abstract":"The NK model introduced by Kauffman (1993) has been widely accepted as a formal model of rugged fitness landscapes. It is shown that the NK model is incapable of accurately modeling an important class of combinatorial optimization problems. Most notable is the limitation in modeling the epistatic relationships that exist in many real-world constrained optimization problems. In addition to introducing a new method of graphically depicting all high dimension fitness landscapes, an extension to the NK model is proposed.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"5 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":"127734175","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}
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}
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}
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}
The performance of particle swarm optimization using an inertia weight is compared with performance using a constriction factor. Five benchmark functions are used for the comparison. It is concluded that the best approach is to use the constriction factor while limiting the maximum velocity Vmax to the dynamic range of the variable Xmax on each dimension. This approach provides performance on the benchmark functions superior to any other published results known by the authors.
{"title":"Comparing inertia weights and constriction factors in particle swarm optimization","authors":"R. Eberhart, Yuhui Shi","doi":"10.1109/CEC.2000.870279","DOIUrl":"https://doi.org/10.1109/CEC.2000.870279","url":null,"abstract":"The performance of particle swarm optimization using an inertia weight is compared with performance using a constriction factor. Five benchmark functions are used for the comparison. It is concluded that the best approach is to use the constriction factor while limiting the maximum velocity Vmax to the dynamic range of the variable Xmax on each dimension. This approach provides performance on the benchmark functions superior to any other published results known by the authors.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"2 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":"132814447","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 non-generational genetic algorithm for multiobjective optimization problems is proposed. For each element in the population a domination count is defined together with a neighborhood density measure based on a sharing function. Those two measures are then nonlinearly combined in order to define the individual's fitness. Numerical experiments with four test-problems taken from the evolutionary multiobjective literature are performed and the results are compared with those obtained by other evolutionary techniques.
{"title":"A non-generational genetic algorithm for multiobjective optimization","authors":"C. Borges, H. Barbosa","doi":"10.1109/CEC.2000.870292","DOIUrl":"https://doi.org/10.1109/CEC.2000.870292","url":null,"abstract":"In this paper a non-generational genetic algorithm for multiobjective optimization problems is proposed. For each element in the population a domination count is defined together with a neighborhood density measure based on a sharing function. Those two measures are then nonlinearly combined in order to define the individual's fitness. Numerical experiments with four test-problems taken from the evolutionary multiobjective literature are performed and the results are compared with those obtained by other evolutionary techniques.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"83 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":"122612987","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 nurse scheduling problem (NSPs) represents a difficult class of multi-objective optimisation problems consisting of a number of interfering objectives between the hospitals and individual nurses. The objective of this research is to investigate difficulties that occur during the solution of NSP using evolutionary algorithms, in particular genetic algorithms (GA). As the solution method a population-less cooperative genetic algorithm (CGA) is taken into consideration. Because contrary to competitive GAs, we have to simultaneously deal with the optimization of the fitness of the individual nurses and also optimization of the entire schedule as the final solution to the problem in hand. To confirm the search ability of CGA, first a simplified version of NSP is examined. Later we report a more complex and useful version of the problem. We also compare CGA with another multi-agent evolutionary algorithm using pheromone style communication of real ants. Finally, we report the results of computer simulations acquired throughout the experiments.
{"title":"Evolutionary algorithms for nurse scheduling problem","authors":"Ahmad Jan, Masahito Yamamoto, A. Ohuchi","doi":"10.1109/CEC.2000.870295","DOIUrl":"https://doi.org/10.1109/CEC.2000.870295","url":null,"abstract":"The nurse scheduling problem (NSPs) represents a difficult class of multi-objective optimisation problems consisting of a number of interfering objectives between the hospitals and individual nurses. The objective of this research is to investigate difficulties that occur during the solution of NSP using evolutionary algorithms, in particular genetic algorithms (GA). As the solution method a population-less cooperative genetic algorithm (CGA) is taken into consideration. Because contrary to competitive GAs, we have to simultaneously deal with the optimization of the fitness of the individual nurses and also optimization of the entire schedule as the final solution to the problem in hand. To confirm the search ability of CGA, first a simplified version of NSP is examined. Later we report a more complex and useful version of the problem. We also compare CGA with another multi-agent evolutionary algorithm using pheromone style communication of real ants. Finally, we report the results of computer simulations acquired throughout the experiments.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"141 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":"128818895","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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