Results 211 to 220 of about 41,851 (257)
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Mathematical analysis of schema survival for genetic algorithms having dual mutation
Soft Computing, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Apoorva Mishra, Anupam Shukla
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Generalizing the notion of schema in genetic algorithms
Artificial Intelligence, 1991zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Schema processing, proportional selection, and the misallocation of trials in genetic algorithms
Information Sciences, 2000zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fogel, David B., Ghozeil, Adam
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A simpler derivation of schema hazard in genetic algorithms
Information Processing Letters, 1995In 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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Study on Genetic Algorithm Based on Schema Mutation and Its Performance Analysis
2009 Second International Symposium on Electronic Commerce and Security, 2009Genetic 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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BSP‐GA: A new Genetic Algorithm for System Optimization and Excellent Schema Selection
Systems Research and Behavioral Science, 2014The 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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Mathematical modeling analysis of genetic algorithms under schema theorem
Journal of Computational Methods in Sciences and Engineering, 2019Genetic 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.
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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 ...
null Yixin Yu, null Jianzhong Wu
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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 ...
null Yixin Yu, null Jianzhong Wu
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Convergence of Algorithm and the Schema Theorem in Genetic Algorithms
1995In 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.
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The Performance Analysis of Genetic Algorithm Based on Schema
2012In 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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