An Upper Bound on the Convergence Rates of Canonical Genetic Algorithms Yong Gao
Abstract
	An upper bound on the rates of convergence to stationary distributions of canonical genetic algorithms is established in terms of the algorithms' control parameters including the population size, the encoding length, and the mutation probability. The upper bound, in conjunction with results in the literature on the performance of GA, indicates that there is a tradeoff between the convergence rate and the long-term performance in genetic algorithms. To achieve this tradeoff, we propose a general framework of GAsin which structural parameters of GAs, such as the population size and the encoding length, can be modified dynamically.

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