A well-defined termination condition is a fundamental component of any Genetic Algorithm, determining when the evolutionary process will conclude. Initially, a GA often progresses rapidly, with significant improvements occurring in early generations. However, this progress tends to saturate over time, with improvements becoming smaller and less frequent. The termination criteria should be chosen to halt the algorithm once a satisfactory solution has been found or further computation is unlikely to yield significant benefits.

Common termination criteria include:

• Stagnation: The algorithm terminates when there has been no improvement in the population’s best fitness for a specified number of consecutive generations (e.g., 50 generations).
• Maximum Generations: The algorithm runs for a fixed, absolute number of generations. This is one of the simplest and most common stopping conditions.
• Target Fitness Value: The algorithm stops when the fitness of the best individual in the population reaches or exceeds a pre-defined target value, indicating that an acceptable solution has been found.

Like many aspects of GA design, the choice of termination criteria is problem-specific. Often, a combination of these conditions is used to ensure a robust and efficient search.