Hands-On Genetic Algorithms with Python

Summary

In this chapter, you were first introduced to the basic flow of the genetic algorithm. We then went over the key components of the flow, which included creating the population, calculating the fitness function, applying the genetic operators, and checking for stopping conditions.

Next, we went over various methods of selection, including roulette wheel selection, stochastic universal sampling, rank-based selection, fitness scaling, and tournament selection, and demonstrated the differences between them.

We continued by reviewing several methods of crossover, such as single-point, two-point, and k-point crossover, as well as ordered crossover and partially matched crossover.

You were then introduced to a number of mutation methods, ...