Hands-On Genetic Algorithms with Python

Summary

In this chapter, you were introduced to constraint satisfaction problems, a close relative of the previously studied combinatorial optimization problems. Then, we explored three classic constraint satisfaction cases – the N-Queen problem, the nurse scheduling problem, and the graph coloring problem. For each of these problems, we followed the now-familiar process of finding an appropriate representation for a solution, creating a class that encapsulates the problem and evaluates a given solution, and creating a genetic algorithm solution that utilizes that class. We ended up with valid solutions for the problems while getting acquainted with the concept of hard constraints versus soft constraints.

So far, we have been looking ...