Summary

In this chapter, you were 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, SUS, rank-based selection, fitness scaling, and tournament selection, and demonstrated the differences between them.

We continued by reviewing several crossover methods, including single-point, two-point, and k-point crossover, as well as OX1 and partially matched crossover.

You were then introduced to several mutation methods, including flip-bit mutation, followed by the swap, inversion, and scramble mutations.

Real-coded genetic algorithms were presented next, along with their specialized chromosome encoding and their custom genetic operators of crossover and mutation.

This was followed by an introduction...