Results 101 to 110 of about 40,532 (179)
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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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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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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Proceedings. International Conference on Power System Technology, 2003 A novel distribution network reconfiguration algorithm, named core schema genetic shortest-path algorithm (CSGSA) is proposed in this paper. It is based on the loads combination method. CSGSA consists of two steps: (1) searching for the optimal power supply paths for a sequence of loads one by one using shortest-path algorithm, and forming a core ...
Yixin Yu, Jianzhong Wu
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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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Genetic Programming and Evolvable Machines, 2004 Genetic Programming (GP) homologous crossovers are a group of operators, including GP one-point crossover and GP uniform crossover, where the offspring are created preserving the position of the genetic material taken from the parents. In this paper we present an exact schema theory for GP and variable-length Genetic Algorithms (GAs) which is ...
Riccardo Poli +2 more
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Convergence of Algorithm and the Schema Theorem in Genetic 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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BSP‐GA: A new Genetic Algorithm for System Optimization and Excellent Schema Selection
Systems Research and Behavioral Science, 2014 The significance of Internet‐of‐Things to Supply Chain Management has been dramatically increasing. The performance of supply chain based on Internet‐of‐Things is largely dependent on its optimization. Genetic algorithms (GAs) are important intelligent methods for complex system optimization problems, but they have some internal drawbacks such as ...
Chenxia Jin +4 more
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Short notes on the schema theorem and the building block hypothesis in genetic algorithms
, 1998 After decades of success, research on evolutionary algorithms aims at developing a sound theory that describes and predict the behavior of these algorithms. One research topic of interest is the analysis of the role of crossover and recombination in genetic algorithms, especially since various papers come to different conclusions.
Ralf Salomon
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Genetic Programming and Evolvable Machines, 2001 Summary: A few schema theorems for genetic programming (GP) have been proposed in the literature in the last few years. Since they consider schema survival and disruption only, they can only provide a lower bound for the expected value of the number of instances of a given schema at the next generation rather than an exact value.
Riccardo Poli
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