Part 2: Solving Problems with Genetic Algorithms

This part focuses on the application of genetic algorithms to various real-world problems using Python, starting with an exploration of the DEAP framework. We begin by tackling the foundational OneMax problem, showcasing the framework’s capabilities. We then move on to more complex combinatorial optimization challenges, such as the traveling salesman and vehicle routing problems, and proceed with an in-depth look at constraint satisfaction problems, including the N-Queen and nurse scheduling problems. The part concludes by applying genetic algorithms to continuous search-space optimization, highlighting advanced techniques such as niching, sharing, and effective constraint management.

This part contains the following chapters:

• Chapter 3, Using the DEAP Framework
• Chapter 4, Combinatorial Optimization
• Chapter 5, Constraint Satisfaction
• Chapter 6, Optimizing Continuous Functions

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