This chapter covers
- Introducing the Gray-coded genetic algorithm
- Understanding real-valued GA and its genetic operators
- Understanding permutation-based GA and its genetic operators
- Understanding multi-objective optimization
- Adapting genetic algorithms to strike a balance between exploration and exploitation
- Solving continuous and discrete problems using GA
This chapter continues with the topic of chapter 7: we will look at various forms of genetic algorithms (GAs) and delve deeper into their real-world applications. We’ll also look at a number of case studies and exercises, such as the traveling salesman problem (TSP), proportional integral derivative (PID) controller design, political districting, the cargo bike loading problem, manufacturing planning, facility allocation, and the opencast mining problem in this chapter and its supplementary exercises included in the online appendix C.