A common approach for learning Bayesian networks (BNs) from data is based on the use of a scoring metric to evaluate the fitness of any given candidate network to the data and a method to explore the search space, which usually is the set of directed acyclic graphs (DAGs). The most efficient search methods used in this context are greedy hill climbing, either deterministic or stochastic. One of...

This work presents a heuristic approach proposed by one of the finalists of the Third International Timetabling Competition (ITC2011). The KHE school timetabling engine is used to generate an initial solution and then Simulated Annealing (SA) and Iterated Local Search (ILS) perform local search around this solution.

Abstract. This article extracts and analyzes local optima networks for the permutation flow-shop problem. Two widely used move operators for permutation representations, namely, swap and insertion, are incorporated into the network landscape model. The performance of a heuristic search algorithm on this problem is also analyzed. In particular, we study the correlation between local optima netwo...

This paper deals with the blocking flow shop problem and proposes an Iterated Local Search (ILS) procedure combined with a variable neighbourhood search (VNS) for the total tardiness minimization. The proposed ILS makes use of a NEH-based procedure to generate the initial solution, uses a local search to intensify the exploration which combines the insertion and swap neighbourhood and uses a pe...