January 2020
Intermediate to advanced
346 pages
9h 8m
English
Content preview from Hands-On Genetic Algorithms with Python
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Continuous learning
In nature, evolution never stops. As the environmental conditions change, the population will adapt to them. Similarly, genetic algorithms can operate continuously in an ever-changing environment, and at any point in time, the best current solution can be fetched and used.
For this to be effective, the changes in the environment need to be slow in relation to the generation turnaround rate of the genetic algorithm-based search. Now that we have covered the advantages, let's look at the limitations of genetic algorithms.
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