A Markov chain analysis of genetic algorithms with power of 2 cardinality alphabets
In this paper we model the run time behavior of GAs using higher cardinality representations as Markov Chains, define the states of the Markov Chain and derive the transition probabilities of the corresponding transition matrix. We analyze the behavior of this chain and obtain bounds on its convergence rate and bounds on the runtime complexity of the GA. We further investigate the effects of using binary versus higher cardinality representation of a search space.
European Journal of Operational Research
A Markov chain analysis of genetic algorithms with power of 2 cardinality alphabets.
European Journal of Operational Research,
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