January 2020
Intermediate to advanced
346 pages
9h 8m
English
Content preview from Hands-On Genetic Algorithms with Python
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Computationally-intensive
Operating on (potentially large) populations and the repetitive nature of genetic algorithms can be computationally intensive, as well as time consuming before a good result is reached.
These can be alleviated with a good choice of hyperparameters, implementing parallel processing, and in some cases, caching the intermediate results.
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