Swapping hill-climbing for tabu search in Bayesian network learning: PCTABU wins on sparse networks and ties on dense ones
Hybrid Bayesian network learning separates skeleton construction from score-based search, and both the search strategy (hill-climbing or tabu) and the scoring function (BDe, LL or BIC) change the learned structure. PCTABU pairs the PC skeleton with tabu search; against PCHC on eight real benchmark networks it did better on the sparser Alarm, Child and Hailfinder, tied on the other five, and LL scored worst for both algorithms while BDe and BIC performed similarly.