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

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

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

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

We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear map; for instance the space of all

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