An approximated simulation algorithm is presented for the propagation of convex sets of probabilities. It is assumed that the graph is such that an exact probabilistic propagation is feasible. The algorithm is a simulated annealing procedure, which randomly selects probability distributions among the possible ones, performing at the same time an exact probabilistic propagation. The algorithm can be applied to general directed acyclic graphs and is carried out on a tree of cliques. Some experimental tests are shown. 1. Introduction One of the main problems with probabilistic propagation algoritms on graphical structures is the introduction of the initial exact conditional probabilities. A number of authors have tried to overcome this difficulty by allowing the use of intervals on the specified probabilities [5, 10, 11, 3, 14, 13, 19, 23, 2]. Some of these works [10, 11, 3, 13, 23] focus on the use of convex sets of probabilities. Convex sets are a more general tool for representing un...

Finding the degree-constrained minimum spanning tree (d-MST) of a graph is a wellstudied NP-hard problem of importance in communications network design and other network-related problems. In this paper we describe some previously proposed algorithms for solving the problem, and then introduce a novel tree construction algorithm called the Randomised Primal Method (RPM) which builds degree-constrained trees of low cost from solution vectors taken as input. RPM is applied in three stochastic iterative search methods: simulated annealing, multi-start hillclimbing, and a genetic algorithm. While other researchers have mainly concentrated on finding spanning trees in Euclidean graphs, we consider the more general case of random graph problems. We describe two random graph generators which produce particularly challenging d-MST problems. On these and other problems we find that the genetic algorithm employing RPM outperforms simulated annealing and multi-start hillclimbing. Our experimental ...

this report I present an algorithm for finding planar segregations of phonemes for particular languages. This algorithm requires no domain-specific knowledge of phonology or phonetics. Despite this lack of knowledge, the implemented algorithm has identified the structurally significant segregations for thirty languages