Hands-On Genetic Algorithms with Python

Probabilistic behavior

While many of the traditional algorithms are deterministic in nature, the rules that are used by genetic algorithms to advance from one generation to the next are probabilistic.

For example, when selecting the individuals that will be used to create the next generation, the probability of selecting a given individual increases with the individual's fitness, but there is still a random element in making that choice. Individuals with low score values can still be chosen as well, although with a lower probability.

Mutation is probability-driven as well, usually occurs with low likelihood, and makes changes at random location(s) in the chromosome.

The crossover operator can have a probabilistic element as well. In some ...