Results 121 to 130 of about 40,532 (179)

Schema genetic algorithm for fractal image compression

Engineering Applications of Artificial Intelligence, 2007
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, Jyh-Horng Jeng, Jer-Guang Hsieh   +2 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.
U.K. Chakraborty, D.G. Dastidar, M.K. Roy   +2 more
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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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Generalizing the notion of schema in genetic algorithms

Artificial Intelligence, 1991
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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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A simpler derivation of schema hazard in genetic algorithms

Information Processing Letters, 1995
In a previous paper we derived an epression for the schema hazard function at any particular generation in the genetic algorithm. A much simpler derivation is presented here. It is more direct and eliminates the need for the use of the multinomial distribution and the multiple Poisson distribution.
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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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The Performance Analysis of Genetic Algorithm Based on Schema

2012
In this paper, for a new genetic algorithm (GA) based on schema (BS-GA), we mainly analyze the performance of BS-GA through simulation. Through two examples, we verify the effectiveness of our algorithm. All the results indicate that, BS-GA is better than standard genetic algorithm (SGA) obviously in computation efficiency and convergence performance.
Chenxia Jin, Jie Yang, Fachao Li
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Mathematical analysis of schema survival for genetic algorithms having dual mutation

Soft Computing, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Apoorva Mishra, Anupam Shukla
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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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