chapter five
In this chapter
- Considering options for the steps in the genetic algorithm life cycle
- Adjusting a genetic algorithm to solve different problems
- Configuringadvanced parameters for a genetic algorithm life cycle
The general life cycle of a genetic algorithm is outlined in chapter 4, which is a prerequisite for this chapter. Here, we consider other problems that a genetic algorithm might be well-suited to solve, why some of the approaches demonstrated so far won’t work, and what alternatives we might try. As a reminder, the general life cycle of a genetic algorithm is
- Creating a population. This step involves generating an initial set of random candidate solutions.
- Measuring the fitness of individuals in the population. This step determines how good a specific solution is by using a fitness function that scores solutions to determine how good they are.
- Selecting parents based on their fitness. This step involves selecting parents that will produce offspring.
- Reproducing individuals from parents. This step generates new candidates by combining the genes of selected parents and introducing random mutations to the offspring.
- Populating the next generation. This step involves selecting individuals and offspring from the population that will survive to the next generation.