Abstract
In this chapter we discuss the theoretical aspects of Estimation of Distribution Algorithms (EDAs). We unify most of the results found in the EDA literature by introducing them into two general frameworks: Markov chains and dynamical systems. In addition, we use Markov chains to give a general convergence result for discrete EDAs. Some discrete EDAs are analyzed using this result, to obtain sufficient conditions for convergence.
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González, C., Lozano, J.A., Larrañaga, P. (2002). Mathematical Modeling of Discrete Estimation of Distribution Algorithms. In: Larrañaga, P., Lozano, J.A. (eds) Estimation of Distribution Algorithms. Genetic Algorithms and Evolutionary Computation, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1539-5_6
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DOI: https://doi.org/10.1007/978-1-4615-1539-5_6
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