Several network flows heuristics have been suggested in the past for the solution of the complementary suppression problem. However, a limited computational experience using them is reported in the literature, and, moreover, they were only appropriate for two-dimensional tables. The purpose of this paper is twofold. First, we perform an empirical comparison of two network flows heuristics. They...

In this paper we present two different parallelization schemes for the SMT solver iSAT, based on (1) the distribution of work by dividing the search space into disjoint parts and exploring them in parallel, thereby exchanging learnt information, and (2) a portfolio approach, where the entire benchmark instance is explored in parallel by several copies of the same solver but using different heur...

We explore the use of iterative cost-shifting as a dynamic heuristic generator for solving MPE in graphical models via Branch and Bound. When mini-bucket elimination is limited by its memory budget, it may not provide good heuristics. This can happen often when the graphical model has a very high induced width with large variable domain sizes. In addition, we explore a hybrid setup where both M...

Ordered Binary Decision Diagrams (OBDDs) are the state-of-the-art data structure in CAD for ICs. OBDDs are very sensitive to the chosen variable ordering, i.e. the size may vary from linear to exponential. In this paper we present an Evolutionary Algorithm (EA) that learns good heuristics for OBDD minimization starting from a given set of basic operations. The diierence to other previous approa...

One of the most successful algorithms in the last few years for solving classical planning problems is Graphplan [3]. This algorithm can be seen as a disjunctive version of forward state space planners. The algorithm has two interleaved phases: a forward phase where a polynomial-time data structure called ”planning graph” is incrementally extended, and a backward phase where that planning graph...

It has been shown in the literature that the task of estimating the parameters of nonlinear models may be tackled with optimization heuristics. Thus, we attempt to carry these intuitions over to the estimation procedure of smooth transition autoregressive (STAR, Teräsvirta, 1994) models by introducing the following three stochastic optimization algorithms: Simulated Annealing, (Kirkpatrick, Gel...

Train scheduling has been a significant issue in the railway industry. Over the last few years, numerous approaches and tools have been developed to compute railway scheduling. In this paper, we present a set of heuristics for a constraint-based train scheduling tool, which is a project in collaboration with the National Network of Spanish Railways RENFE , Spain. We formulate train scheduling a...

For many real-world problems, there exist non-deterministic heuristics which generate valid but possibly sub-optimal solutions. The program optimisation with dependency injection method, introduced here, allows such a heuristic to be placed under evolutionary control, allowing search for the optimum. Essentially, the heuristic is “fooled” into using a genome, supplied by a genetic algorithm, in...

The main objective of the Multi Job Shop Scheduling problem (MJSSP) is to find a schedule of operations that can minimize the final completion time. In this paper, the various approaches with heuristics used to solve MJSSP are studied and its constraints clearly represented in mathematical model. MJSSP has been implemented with Steepest-Ascent Hill Climbing(SAHC) algorithm with constructive heu...

Abs t rac t . The goal of this paper is twofold. First, we introduce a class of local search procedures for solving optimization and constraint problems. These procedures are based on various heuristics for choosing variables and values in order to examine a general neighborhood. Second, four combinations of heuristics are empirically evaluated by using the graph-coloring problem and a real wor...