Results 221 to 230 of about 656,169 (307)
Generalizing the notion of schema in genetic algorithms
Artificial Intelligence, 1991 Abstract In this paper we examine some of the fundamental assumptions which are frequently used to explain the practical success which Genetic Algorithms (GAs) have enjoyed. Specifically, the concept of schema and the Schema Theorem are interpreted from a new perspective.
Michael D. Vose
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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 processing, proportional selection, and the misallocation of trials in genetic algorithms
Information Sciences, 2000 Abstract Traditional selection in genetic algorithms has relied on reproduction in proportion to observed fitness. There has been recent interest in assessing the result of proportional selection on schemata in the presence of random effects (e.g., noisy evaluation of solutions).
David B. Fogel, Adam Ghozeil
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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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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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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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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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SCHEMA ANALYSIS OF GENETIC ALGORITHMS ON MULTIPLICATIVE LANDSCAPE
Asia-Pacific Conference on Simulated Evolution and Learning, 2004 Hiroshi Furutani
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