Abstract
This chapter describes parallel versions of some Estimation of Distribu-tion Algorithms (EDAs). We concentrate on those algorithms that use Bayesian networks to model the probability distribution of the selected individuals, and particularly on those that use a score+search learning strategy. Apart from the evaluation of the fitness function, the biggest computational cost in these EDAs is due to the structure learning step. We aim to speed up the structure learning step by the use of parallelism. Two different approaches will be given and evaluated experimentally in a shared memory MIMD computer.
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Lozano, J.A., Sagarna, R., Larrañaga, P. (2002). Parallel 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_5
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DOI: https://doi.org/10.1007/978-1-4615-1539-5_5
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