Abstract
The paper deals with the Bivariate Marginal Distribution Algorithm (BMDA). BMDA is an extension of the Univariate Marginal Distribution Algorithm (UMDA). It uses the pair gene dependencies in order to improve algorithms that use simple univariate marginal distributions. BMDA is a special case of the Factorization Distribution Algorithm, but without any problem specific knowledge in the initial stage. The dependencies are being discovered during the optimization process itself. In this paper BMDA is described in detail. BMDA is compared to different algorithms including the simple genetic algorithm with different crossover methods and UMDA. For some fitness functions the relation between problem size and the number of fitness evaluations until convergence is shown.
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© 1999 Springer-Verlag London Limited
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Pelikan, M., Muehlenbein, H. (1999). The Bivariate Marginal Distribution Algorithm. In: Roy, R., Furuhashi, T., Chawdhry, P.K. (eds) Advances in Soft Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0819-1_39
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DOI: https://doi.org/10.1007/978-1-4471-0819-1_39
Publisher Name: Springer, London
Print ISBN: 978-1-85233-062-0
Online ISBN: 978-1-4471-0819-1
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