January 2020
Intermediate to advanced
346 pages
9h 8m
English
Content preview from Hands-On Genetic Algorithms with Python
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Parallelism
Genetic algorithms lend themselves well to parallelization and distributed processing. Fitness is independently calculated for each individual, which means all the individuals in the population can be evaluated concurrently.
In addition, the operations of selection, crossover, and mutation can each be performed concurrently on individuals and pairs of individuals in the population.
This makes the approach of genetic algorithms a natural candidate for distributed as well as cloud-based implementation.
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