A Directed Genetic Algorithm for global optimization

Within the framework of real-coded genetic algorithms, this paper proposes a directed genetic algorithm (DGA) that introduces a directed crossover operator and a directed mutation operator. The operation schemes of these operators borrow from the reflection and the expansion search mode of the Nelde...

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Bibliographic Details
Published in:Applied mathematics and computation Vol. 219; no. 14; pp. 7348 - 7364
Main Authors: Kuo, Hsin-Chuan, Lin, Ching-Hai
Format: Journal Article
Language:English
Published: Elsevier Inc 15.03.2013
Subjects:
Cantilever beams
Directed genetic algorithm
Genetic algorithms
Global optimization
Mathematical analysis
Mathematical models
Nelder–Mead’s simplex algorithm
Operators
Optimization
Searching
Strategy
Nelder–Mead’s simplex algorithm
Global optimization
Directed genetic algorithm
ISSN:0096-3003, 1873-5649
Online Access:Get full text
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Summary:Within the framework of real-coded genetic algorithms, this paper proposes a directed genetic algorithm (DGA) that introduces a directed crossover operator and a directed mutation operator. The operation schemes of these operators borrow from the reflection and the expansion search mode of the Nelder–Mead’s simplex method. First, the Taguchi method is employed to study the influence analysis of the parameters in the DGA. The results show that the parameters in the DGA have strong robustness for solving the global optimal solution. Then, several strategies are proposed to enhance the solution accuracy capability of the DGA. All of the strategies are applied to a set of 30/100-dimensional benchmark functions to prove their superiority over several genetic algorithms. Finally, a cantilevered beam design problem with constrained conditions is used as a practical structural optimization example for demonstrating the very good performance of the proposed method.
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ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2012.12.046