11 research outputs found
A feature-based comparison of local search and the Christofides algorithm for the travelling salesperson problem
Understanding the behaviour of well-known algorithms for classical NP-hard optimisation problems is still a difficult task. With this paper, we contribute to this research direction and carry out a feature based comparison of local search and the well-known Christofides approximation algorithm for the Traveling Salesperson Problem. We use an evolutionary algorithm approach to construct easy and hard instances for the Christofides algorithm, where we measure hardness in terms of approximation ratio. Our results point out important features and lead to hard and easy instances for this famous algorithm. Furthermore, our cross-comparison gives new insights on the complementary benefits of the different approaches.Samadhi Nallaperuma, Markus Wagner, Frank Neumann, Bernd Bischl, Olaf Mersmann, Heike Trautmannhttp://www.sigevo.org/foga-2013
Improved Fixed-Budget Results via Drift Analysis
Fixed-budget theory is concerned with computing or bounding the fitness value
achievable by randomized search heuristics within a given budget of fitness
function evaluations. Despite recent progress in fixed-budget theory, there is
a lack of general tools to derive such results. We transfer drift theory, the
key tool to derive expected optimization times, to the fixed-budged
perspective. A first and easy-to-use statement concerned with iterating drift
in so-called greed-admitting scenarios immediately translates into bounds on
the expected function value. Afterwards, we consider a more general tool based
on the well-known variable drift theorem. Applications of this technique to the
LeadingOnes benchmark function yield statements that are more precise than the
previous state of the art.Comment: 25 pages. An extended abstract of this paper will be published in the
proceedings of PPSN 202
Application of a Knowledge Discovery Process to Study Instances of Capacitated Vehicle Routing Problems
Vehicle Routing Problems (VRP) are computationally challenging, constrained optimization problems, which have central role in logistics management. Usually different solvers are being developed and applied for different kind of problems. However, if descriptive and general features could be extracted to describe such problems and their solution attempts, then one could apply data mining and machine learning methods in order to discover general knowledge on such problems. The aim then would be to improve understanding of the most important characteristics of VRPs from both efficient solution and utilization points of view. The purpose of this article is to address these challenges by proposing a novel feature analysis and knowledge discovery process for Capacitated Vehicle Routing problems (CVRP). Results of knowledge discovery allow us to draw interesting conclusions from relevant characteristics of CVRPs.peerReviewe