Results 201 to 210 of about 41,851 (257)

GATuner: Tuning Schema Matching Systems Using Genetic Algorithms

2010 2nd International Workshop on Database Technology and Applications, 2010
Most recent schema matching systems combine multiple components, each of which employs a particular matching technique with several knobs. The multi-component nature has brought tuning problems for domain users. In this paper, we present GATuner, an approach to automatically tune schema matching systems using genetic algorithms. We match a given schema
Yuting Feng, Lei Zhao, Jiwen Yang
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Schema analysis of multi-points crossover genetic algorithm

Proceedings of the 3rd World Congress on Intelligent Control and Automation (Cat. No.00EX393), 2002
An extended schema theorem concerning two-point crossover and uniform crossover is deduced from one-point crossover schema theorem, and the conclusion that uniform crossover has advantage over two-point crossover and one-point crossover is made. The extended schema theorem provides theoretic foundation for the experimental results of Syswerda (1989).
null Xu Hongze, null Wei Xue Ye, null Xu Maosheng   +2 more
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Optinformatics for schema analysis of binary genetic algorithms

Proceedings of the 10th annual conference on Genetic and evolutionary computation, 2008
Given the importance of optimization and informatics which are the two broad fields of research, we present an instance of Optinformatics which denotes the specialization of informatics for the processing of data generated in optimization so as to extract possibly implicit and potentially useful information and knowledge.
Minh Nghia Le, Yew Soon Ong, Quang Huy Nguyen   +2 more
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The schema deceptiveness and deceptive problems of genetic algorithms

Science in China Series F Information Sciences, 2001
Genetic algorithms (GA) are a new type of global optimization methodology based on nature selection and heredity, and its power comes from the evolution process of the population of feasible solutions by using simple genetic operators. The past two decades saw a lot of successful industrial cases of GA application, and also revealed the urgency of ...
Minqiang Li, Jisong Kou
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Problem-Independent Schema Synthesis for Genetic Algorithms

2003
As a preprocessing for genetic algorithms, static reordering helps genetic algorithms effectively create and preserve high-quality schemata, and consequently improves the performance of genetic algorithms. In this paper, we propose a static reordering method independent of problem-specific knowledge.
Yong-Hyuk Kim, Yung-Keun Kwon, Byung-Ro Moon   +2 more
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Schema survival rates and heuristic search in genetic algorithms

[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence, 2002
Genetic algorithms are a relatively new paradigm for search in artificial intelligence. It is shown that, for certain kinds of search problems, called permutation problems, the ordinary rule for intermixing the genes between two organisms leads to longer search chains than are necessary. A schema is a partially completed organism.
B.P. Buckles, F.E. Petry, R.L. Kuester
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Mapping XML Schema to Relations Using Genetic Algorithm

2004
As web-applications grow in number and complexity, there is a need for efficient mappings from XML schemas to the flat relational tables so that existing functions in relational database systems can be utilized. However, many of the existing mapping methods, such as the model-based or the structure-based methods, do not exploit query history for better
Vincent Ng, Chan Chi Kong, Stephen Chan
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Schema representation in virus-evolutionary genetic algorithm for knapsack problem

1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), 2002
This paper deals with a genetic algorithm based on virus theory of evolution (VE-GA). VE-GA simulates coevolution of a host population of candidate solutions and a virus population of substring representing schemata. In the coevolutionary process, the virus individuals propagate partial genetic information in the host population by virus infection ...
N. Kubota, T. Fukuda
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A reliability analysis of schema processing in genetic algorithms

TENCON'92 - Technology Enabling Tomorrow, 2003
An analysis of schema processing in simple genetic algorithms is presented. The hazard function (instantaneous failure rate) of a schema under fitness-proportionate selection, single-point crossover and mutation is computed, and the reliability expression is derived from the hazard function.
U.K. Chakraborty, D.G. Dastidar, M.K. Roy   +2 more
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hGRGA: A scalable genetic algorithm using homologous gene schema replacement

Swarm and Evolutionary Computation, 2017
Abstract In this article, we propose a new evolutionary algorithm, referred as h omologous G ene R eplacement G enetic A lgorithm (hGRGA) that includes a novel and generic operator called h omologous G ene R eplacement (hGR). The hGR operator improves the chromosomes in gene level to promote their overall functionality.
Sumaiya Iqbal, Md Tamjidul Hoque
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