Results 171 to 180 of about 347,065 (259)
Generalizing the notion of schema in genetic algorithms
Artificial Intelligence, 1991 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Michael D. Vose
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Schema processing, proportional selection, and the misallocation of trials in genetic algorithms
Information Sciences, 2000 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
David B. Fogel, Adam Ghozeil
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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 +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.
Bill P. Buckles +2 more
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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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Convergence of Algorithm and the Schema Theorem in Genetic Algorithms
International Conference on Adaptive and Natural Computing Algorithms, 1995 In this article two aspects of GA are commented from a mathematical point of view. One is concerned with the convergence of GA, and the other is a probabilistic interpretation of the schema theorem. GA produces a stochastic process (that is, Markov chain) of populations.
Yoshinori Uesaka
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Analysis of Schema Formation in Genetic Algorithms: A Review
International Conference on Genetic and Evolutionary ComputingTiancong Zhang +4 more
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Recursive Conditional Schema Theorem, Convergence and Population Sizing in Genetic Algorithms
Foundations of Genetic Algorithms, 2001 Abstract In this paper we start by presenting two forms of schema theorem in which expectations are not present. These theorems allow one to predict with a known probability whether the number of instances of a schema at the next generation will be above a given threshold.
Riccardo Poli
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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.
Uday K. Chakraborty +2 more
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Mathematical modeling analysis of genetic algorithms under schema theorem
Journal of Computational Methods in Sciences and Engineering, 2019Genetic algorithm (GA) is a search algorithm for solving optimization problems and is an important part of evolutionary algorithms (EA). The main purpose of this research is to improve the mathematical modeling of GA, and to explore how to overcome the ...
Donghai Liu
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