Review of Industrial Design Optimization by Genetic Algorithms-Reference-Cited by-同舟云学术

Review of Industrial Design Optimization by Genetic Algorithms

Published:2019 Issue: Volume: Page:336-346
ISSN:2195-4356
Container-title:Advances on Mechanics, Design Engineering and Manufacturing II
language:
Short-container-title:

Author:

Sáez-Gutiérrez F. L.,Cañavate F. J. F.,Guerrero-González A.

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-12346-8_33

Reference50 articles.

1. Kim SJ, Lee JH (2017) A study on metadata structure and recommenders of biological systems to support bio-inspired design. Eng Appl Artif Intell 57:16–37

2. Guizzo G, Vergilio SR (2018) A pattern-driven solution for designing multi-objective evolutionary algorithms. Nat Comput 1–14

3. Chaturvedi P, Kumar P (2015) Control parameters and mutation based variants of differential evolution algorithm. J Comput Methods Sci Eng 15(4): 783–800

4. Pavai G, Geetha TV (2018) New crossover operators using dominance and co-dominance principles for faster convergence of genetic algorithms. Soft Comput 1–26

5. Hanh LTM, Binh NT, Tung KT (2016) A novel fitness function of metaheuristic algorithms for test data generation for simulink models based on mutation analysis. J Syst Softw 120:17–30

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