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Analysis of Schema Formation in Genetic Algorithms: A Review

International Conference on Genetic and Evolutionary Computing
Tiancong Zhang, Shu-Chuan Chu, Shaowei Weng, Thi-Kien Dao, Trong-The Nguyen   +4 more
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Mathematical modeling analysis of genetic algorithms under schema theorem

Journal of Computational Methods in Sciences and Engineering, 2019
Genetic 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 shortcomings of traditional algorithms under the Schema Theorem.
Donghai Liu
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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.
M. Vose
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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.
U. Chakraborty
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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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Schema theorem of real-coded nonlinear genetic algorithm

Proceedings. International Conference on Machine Learning and Cybernetics, 2003
Through the mechanism analysis of simple genetic algorithm (SGA), every genetic operator can be considered as a linear function. So some disadvantages of SGA may be solved if the genetic operators are modified to a nonlinear function. According to the above method, a nonlinear genetic algorithm is introduced.
null Zhi-Hua Cui, null Jian-Chao Zeng
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The circular schema theorem for genetic algorithms and two-point crossover

Second International Conference on Genetic Algorithms in Engineering Systems, 1997
The schema theorem is the classical formulation of the search strategy performed by genetic algorithms (adaptation procedures mimicking biological evolution and molecular genetics). The original schema theorem has been derived for single-point crossover assuming that the individual chromosomes are arranged as strings.
A. Neubauer
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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.
Fogel, David B., Ghozeil, Adam
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Genetic algorithm with modified reproduction strategy based on self-organizing map and usable schema

International Congress Series, 2006
Abstract In this paper, we propose a new updating method considering usability of each element of inputs and apply it to the reproduction of the GA to achieve more effective search than the traditional reproduction. In the proposed updating method, the order of updating elements is decided by averaging the corresponding elements multiplied by fitness
Ryosuke Kubota, Keiichi Horio, Takeshi Yamakawa   +2 more
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