January 2020
Intermediate to advanced
346 pages
9h 8m
English
Content preview from Hands-On Genetic Algorithms with Python
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Resilience to noise
Some problems present noisy behavior. This means that, even for similar input parameter values, the output value may be somewhat different every time it's measured. This can happen, for example, when the data that's being used is being read from sensor outputs, or in cases where the score is based on human opinion, as was discussed in the previous section.
While this kind of behavior can throw off many traditional search algorithms, genetic algorithms are generally resilient to it, thanks to the repetitive operation of reassembling and reevaluating the individuals.
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