Mutation is a genetic-algorithm operation that makes a random change to a candidate solution. It introduces new possibilities that crossover alone might not produce. section 2.5
Delivery planning
A candidate delivery plan might contain routes, resource allocations, and schedules. After strong plans are selected and combined through crossover, mutation could randomly alter one plan, producing a new route or allocation that reveals a better strategy. section 2.5
Mutation participates in an iterative optimization process:
- Start with a population of potential solutions. section 2.5
- Evaluate and select candidates using factors such as speed and cost. section 2.5
- Combine selected solutions, apply mutation, and repeat across generations until improvement stops or a cycle limit is reached. section 2.5
Figure 2.2 Flowchart of a genetic algorithm
Use the flowchart to follow mutation’s place in the genetic-algorithm cycle. section 2.5
