Results 171 to 180 of about 12,025 (251)
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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Parallel Genetic Algorithms with Schema Migration
Proceedings 26th Annual International Computer Software and Applications, 2003 Genetic algorithms (GAs) are efficient non-gradient stochastic search methods. Parallel GAs are proposed to overcome the deficiencies of sequential GAs, such as low speed and aptness to locally converge. However the tremendous communication cost incurred offsets the advantages of parallel GAs.
Guan Yu
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Genetic algorithm based on schema comparison
Journal of Computer Applications, 2008 Jin-rong XU
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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 genetic algorithm for fractal image compression
Engineering Applications of Artificial Intelligence, 2006 In this paper, fractal image compression using schema genetic algorithm (SGA) is proposed. Utilizing the self-similarity property of a natural image, the partitioned iterated function system (PIFS) will be found to encode an image through genetic algorithm (GA) method.
Ming-Sheng Wu +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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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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ON THE FITNESS OF HIGH ORDER SCHEMA OF A LINEAR-WEIGHTED CODED GENETIC ALGORITHM
Computational Intelligence in Decision and Control, 2008 According to Schema Theorem, the larger the fitness value of a schema is, the higher the chance of the sub-space corresponding to the schema being chosen for searching is. Therefore, the coding of a genetic algorithm should be designed to produces short building blocks at as more fixed positions of the strings as possible.
Hongqiang Mo +4 more
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Study on Genetic Algorithm Based on Schema Mutation and Its Performance Analysis
2009 Second International Symposium on Electronic Commerce and Security, 2009 Genetic algorithm (GA), as a kind of important intelligence computing tool, is a wide research content in the academic circle and the application domain now. In this paper, for the mutation operation of GA, by combining with the essential feature, we establish a genetic algorithm based on schema mutation (denoted by SM-GA, for short).
Fachao Li, Tingyu Zhang
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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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