5 Advanced evolutionary approaches
This chapter covers
- Considering options for the various steps in the genetic algorithm life cycle
- Adjusting a genetic algorithm to solve varying problems
- The advanced parameters for configuring a genetic algorithm life cycle based on different scenarios, problems, and datasets
NOTE
Chapter 4 is a prerequisite to this chapter.
Evolutionary algorithm life cycle
The general life cycle of a genetic algorithm is outlined in chapter 4. In this chapter, we consider other problems that may be suitable to be solved with a genetic algorithm, why some of the approaches demonstrated thus far won’t work, and alternative approaches.
As a reminder, the general life cycle of a genetic algorithm is as follows:
- Creating a population—Creating a random population of potential solutions.
- Measuring fitness of individuals in the population—Determining how good a specific solution is. This task is accomplished by using a fitness function that scores solutions to determine how good they are.
- Selecting parents based on their fitness—Selecting pairs of parents that will reproduce offspring.
- Reproducing individuals from parents—Creating offspring from their parents by mixing genetic information and applying slight mutations to the offspring.
- Populating the next generation—Selecting individuals and offspring from the population that will survive to the next generation.
Keep the life cycle flow (depicted in figure 5.1) in mind as we work through this chapter.