Hands-On Genetic Algorithms with Python

Constraint Satisfaction

In this chapter, you will learn how genetic algorithms can be utilized for solving constraint satisfaction problems. We will start by describing the concept of constraint satisfaction and how it applies to search problems and combinatorial optimization. Then, we will look at several hands-on examples of constraint satisfaction problems and their Python-based solutions using the DEAP framework. The problems we will cover include the well-known N-Queen problem, followed by the nurse scheduling problem, and finally the graph coloring problem. Along the way, we will learn the difference between hard and soft constraints, as well as how they can be incorporated into the solution process.

In this chapter, we will cover the ...