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In practice, genetic algorithms have had a widespread impact on optimization problems,
such as circuit layout and job-shop scheduling. At present, it is not clear whether the appeal
of genetic algorithms arises from their performance or from their æsthetically pleasing origins
in the theory of evolution. Much work remains to be done to identify the conditions under
which genetic algorithms perform well.
In this category, two genetic algorithms are implemented to solve the 8-queen puzzle according to the idea
shown in Figure 4.6 (pp.127) and Figure 4.8 (pp129) in [1] while I am trying to help the students in my AI class, at Xiangtan University, to make sense such difficult content.
% [1]S. Russell and P. Norvig, “Artificial intelligence: a modern approach,” 2002.
Demo.m --------------------------The example given in Figure 4.6, in [1];
GA8Queens.m----------------The implemented genetic algorithm according to Figure 4.8 in [1];
GA8Queens.m1--------------The enhanced version of 'GA8Queens.m'
Zitieren als
Chixin Xiao (2026). To solve the 8-Queen problem by using Genetic Algorithm (https://de.mathworks.com/matlabcentral/fileexchange/109530-to-solve-the-8-queen-problem-by-using-genetic-algorithm), MATLAB Central File Exchange. Abgerufen .
Allgemeine Informationen
- Version 1.0.3 (4,22 KB)
Kompatibilität der MATLAB-Version
- Kompatibel mit allen Versionen
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| Version | Veröffentlicht | Versionshinweise | Action |
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| 1.0.3 | Append the logo image |
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| 1.0.2 | update tags and the title |
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| 1.0.1 | Revise the title |
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| 1.0.0 |