Selection retains comparatively strong candidate solutions for further evolution. A genetic algorithm evaluates solutions by fitness, sorts them by performance, and chooses a group for crossover. section 2.5
Supply-chain route planning
For product delivery, candidate solutions can represent routes, distribution plans, and schedules. After comparing delivery time, cost, and other factors, selection keeps stronger plans for crossover; mutation can introduce new strategies. Repeating these generations continues until improvement stops or a chosen cycle limit is reached. section 2.5
Figure 2.2 Flowchart of a genetic algorithm
Use the flowchart to trace evaluation, selection, crossover, mutation, and repeated generations as one optimization cycle. section 2.5
