January 2020
Intermediate to advanced
346 pages
9h 8m
English
Content preview from Hands-On Genetic Algorithms with Python
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Use cases of genetic algorithms
Based on the material we covered in the previous sections, genetic algorithms are best suited for the following types of problems:
- Problems with complex mathematical representation: Since genetic algorithms only require the outcome of the fitness function, they can be used for problems with target functions that are hard or impossible to differentiate, problems with a large number of parameters, and problems with a mix of parameter types.
- Problems with no mathematical representation: Genetic algorithms don't require a mathematical representation of the problem as long as a score value can be obtained or a method is available to compare two solutions.
- Problems involving a noisy environment: Genetic algorithms ...
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