Reinforcement Learning Algorithms with Python

Genetic algorithms

The idea of GAs is very straightforward—evaluate the current generations, use only the top-performing individuals to generate the next candidate solutions, and discard the other individuals. This is shown in the preceding diagram. The survivors will generate the next population by crossover and mutation. These two processes are represented in the following diagram. Crossover is done by selecting two solutions among the survivors and combining their parameters. Mutation, on the other hand, involves changing a few random parameters on the offspring's genotype:

Figure 11.3. Visual illustration of mutation and crossover

Crossover ...