  Estimation of distribution algorithms (EDAs) constitute a new branch of
evolutionary optimization algorithms, providing effective and efficient
optimization performance in a variety of research areas. Recent studies have
proposed new EDAs that employ mutation operators in standard EDAs to increase
the population diversity. We present a new mutation operator, a matrix
transpose, specifically designed for Bayesian structure learning, and we
evaluate its performance in Bayesian structure learning. The results indicate
that EDAs with transpose mutation give markedly better performance than
conventional EDAs.
