Summary

In this chapter, you were introduced to constraint satisfaction problems, which are close relatives 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 into discrete search problems consisting of states and state transitions. In the next chapter, we will study search problems in a continuous space, to demonstrate the versatility of the genetic algorithms approach.