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A New Stopping Criterion for Genetic Algorithms

Obtaining theoretically legitimate stopping criteria is a difficult task. Being able to use such criteria, especially in real-encoding context, remains an open problem. The proposed criterion is based on a Markov chain modelling and on the distribution of the number of occurrences of the locally best solution during several generations under the assumption of non-convergence. The algorithm stops when the probability of obtaining the observed number of occurrences is too small. The obtained criterion is able to fit very different solution spaces and fitness functions (within studied limitations) without any required user intervention.