Solving engineering optimization problems using an improved real-coded genetic algorithm (IRGA) with directional mutation and crossover-Reference-Cited by-同舟云学术

Solving engineering optimization problems using an improved real-coded genetic algorithm (IRGA) with directional mutation and crossover

Published:2021-01-09 Issue:7 Volume:25 Page:5455-5481
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Das Amit Kumar,Pratihar Dilip Kumar^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

http://link.springer.com/content/pdf/10.1007/s00500-020-05545-9.pdf

Reference81 articles.

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3. Belegundu AD, Arora JS (1985) A study of mathematical programming methods for structural optimization. Part I: theory. Int J Numer Meth Eng 21:1583–1599. https://doi.org/10.1002/nme.1620210904

4. Berry A, Vamplew P (2004) PoD can mutate: a simple dynamic directed mutation approach for genetic algorithms. In: The 2nd international conference on artificial intelligence in science and technology (AISAT 2004). pp 200–205

5. Canayaz M, Karci A (2016) Cricket behaviour-based evolutionary computation technique in solving engineering optimization problems. Appl Intell 44:362–376. https://doi.org/10.1007/s10489-015-0706-6

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