A major difficulty in evaluating incomplete local search style algorithms for constraint satisfaction problems is the need for a source of hard problem instances that are guaranteed to be satisfiable. A standard approach to evaluate incomplete search methods has been to use a general problem

• Evolution is usually depicted by phylogenetic trees; however: 1. evolution is not always tree-like (hybridization, horizontal gene transfer,...) 2. there may be many equally good trees; • Phylogenetic networks [2] are more appropriate in these cases; • We focus here on the minimum common supergraph approach, initiated by Cassens et al. [1] and formalised by Labarre [3];

instances. We consider the problem of selecting a single restart schedule to use in solving each instance in a set of instances. We present offline algorithms for computing an (approximately) optimal restart schedule given knowledge of each instance’s run length distribution, generalization bounds

This article presents a theory in which automatization is construed as the acquisition of a domain-specific knowledge base, formed of separate representations, instances, of each exposure to the task. Processing is considered automatic if it relies on retrieval of stored instances, which will occur

Abstract. Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances

Our aim is to investigate the factors which determine the intrinsic hardness of constructing a solution to any particular constraint satisfaction problem instance, regardless of the algorithm employed. The line of reasoning is roughly the following: There exists a set of distinct, possibly

Taking the Next Release Problem (NRP) as a case study, we intend to analyze the relationship between heuristics and the software engineering problem instances. We adopt an evolutionary algorithm to evolve NRP instances that are ei-ther hard or easy for the target heuristic (GRASP in this study

The tools of molecular biology are used to solve an instance of the directed Hamiltonian path problem. A small graph is encoded in molecules of DNA and the `operations' of the computation are performed with standard protocols and enzymes. This experiment demonstrates the feasibility

It is well known that for many NP-complete problems, such as K-Sat, etc., typical cases are easy to solve; so that computationally hard cases must be rare (assuming P != NP). This paper shows that NP-complete problems can be summarized by at least one "order parameter", and that the hard

This not explores the problem of confining a program during its execution so that it cannot transmit information to any other program except its caller. A set of examples attempts to stake out the boundaries of the problem. Necessary conditions for a solution are stated and informally justified.