The Experts below are selected from a list of 6 Experts worldwide ranked by ideXlab platform
Souguil Ann - One of the best experts on this subject based on the ideXlab platform.
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Modified delta coding algorithm for real parameter optimisation
Electronics Letters, 1995Co-Authors: Soo-ik Chae, Souguil AnnAbstract:A new cost function and a random selector suitable for real parameter optimisation using the delta coding algorithm are presented. The cost function represents the degree to which a Candidate Partial Solution evolves compared with the Partial Solution. The random selector randomly selects one Candidate Partial Solution as the next Partial Solution.
Soo-ik Chae - One of the best experts on this subject based on the ideXlab platform.
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Modified delta coding algorithm for real parameter optimisation
Electronics Letters, 1995Co-Authors: Soo-ik Chae, Souguil AnnAbstract:A new cost function and a random selector suitable for real parameter optimisation using the delta coding algorithm are presented. The cost function represents the degree to which a Candidate Partial Solution evolves compared with the Partial Solution. The random selector randomly selects one Candidate Partial Solution as the next Partial Solution.
Hitoshi Kanoh - One of the best experts on this subject based on the ideXlab platform.
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Evolutionary Design of Cellular Automata using Knowledge-based Genetic Algorithms
Transactions of the Japanese Society for Artificial Intelligence, 2006Co-Authors: Daisuke Ichiba, Hitoshi KanohAbstract:In this paper, we address a Solution to density classification tasks using knowledge-based genetic algorithms. Cellular automata (CAs) are used as models of self -organization and emergent computation, and known to have capacity to solve complex problems. It is, however, very difficult to design transition rules that respond to the user's requests, and it prevents the practical application of CAs. Therefore automatic generation of transition rules is studied. We propose a new method to obtain transition rules using knowledge-based genetic algorithms. The knowledge here is a Candidate Partial Solution of the final Solution. As a result of infection, the genes of a Partial Solution are substituted for those of an individual. The purpose of this study is to obtain rules faster than traditional methods. We use the majority decision rule for the knowledge. Experimental results for density classification tasks prove that the proposed method is faster than a conventional method. In addition, the evidence is given that the best transition rules emerge by the Partial evolution of the majority decision rule.
Daisuke Ichiba - One of the best experts on this subject based on the ideXlab platform.
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Evolutionary Design of Cellular Automata using Knowledge-based Genetic Algorithms
Transactions of the Japanese Society for Artificial Intelligence, 2006Co-Authors: Daisuke Ichiba, Hitoshi KanohAbstract:In this paper, we address a Solution to density classification tasks using knowledge-based genetic algorithms. Cellular automata (CAs) are used as models of self -organization and emergent computation, and known to have capacity to solve complex problems. It is, however, very difficult to design transition rules that respond to the user's requests, and it prevents the practical application of CAs. Therefore automatic generation of transition rules is studied. We propose a new method to obtain transition rules using knowledge-based genetic algorithms. The knowledge here is a Candidate Partial Solution of the final Solution. As a result of infection, the genes of a Partial Solution are substituted for those of an individual. The purpose of this study is to obtain rules faster than traditional methods. We use the majority decision rule for the knowledge. Experimental results for density classification tasks prove that the proposed method is faster than a conventional method. In addition, the evidence is given that the best transition rules emerge by the Partial evolution of the majority decision rule.
