Network Design

(Günther Raidl, Martin Gruber, Bin Hu, Markus Leitner)

A commercial computer network can be represented by an undirected, weighted graph G=(V, E), with node set V and edge set E representing all possible connections. Each edge e from E has associated nonnegative cost(e). Many graph problems seek a subset C of the graph's edges that satisfies a set of constraints and has minimum total weight.

Topics of Research

We concentrate on solving the following optimization problems as efficiently as possible by the means of: evolutionary algorithms, exact techniques (e.g. branch-and-cut-and-price) and the combination of the last two.

Constrained Minimum Spanning Tree Problems

  • Bounded Diameter Minimum Spanning Tree Problem (BDMST): The goal is to find a diameter-constrained minimum spanning tree. In general, the diameter of a graph is the length (number of edges) of the "longest shortest path" between any two nodes of this graph. Although the task of finding an unconstrained MST is relatively easy (Kruskal, Prim), the computation of a BDMST is known to be NP-hard for diameters equal or greater than 4.

    The BDMST problem is a combinatorial optimization problem appearing in applications such as wire-based communication network design when certain aspects of quality of service - like signal-to-noise ratio - have to be considered, in ad-hoc wireless networks, and in the areas of data compression and distributed mutual exclusion algorithms.

    BDMST diameter 14 BDMST diameter 4
    Unconstrained minimum spanning tree,
    diameter = 14.
    Minimum spanning tree with a
    diameter constraint of 4.

    To solve this problem to proven optimality various approaches exist, mostly relying on flow-based multi-commodity mixed integer linear programming formulations or models based on lifted Miller-Tucker-Zemlin inequalities. We suggested a branch-and-cut algorithm based on a compact 0-1 integer linear programming formulation [PDF]. Currently we are enhancing this basic model with additional cuts and a primal heuristics.

    Due to the complexity of the BDMST problem the applicability of exact algorithms is, in practice, restricted to instances with less than 100 nodes when considering complete graphs. Therefore, we also investigated different (meta-)heuristics.

    Together with Bryant Julstrom two evolutionary algorithms were developed, [PDF] and [PDF]. Based on four different neighborhoods for the BDMST problem we also proposed a general variable neighborhood search approach [PDF]. At the moment we exploit the idea of a new encoding of solutions for a new evolutionary algorithm and an ant colony optimization, a joint work with Jano van Hemert.

  • Degree-constrained Minimum Spanning Tree Problem: Find a minimum-cost spanning tree where each node has degree no grater than a bound d (d > 2).

  • Capacitated Minimum Spanning Tree: Find a minimum-cost spanning tree with additional constraints on the sizes of the subtrees incident to the given root node.

  • Optimum Communication Spanning Tree Problem: Find a spanning tree that minimizes the cost of transmitting a given set of communication requirements between nodes.

Generalized Network Design Problems

In contrast to classical Network Design Problems, the generalized versions deal with graphs with node set partitioned into clusters. Depending on the variant, the goal is to find a subgraph containing exactly or at least one node of each cluster satisfying other constraints.
  • Generalized Minimum Spanning Tree Problem: The subgraph is a spanning tree of minimal costs.
  • Generalized Traveling Salesman Problem: The subgraph is a roundtrip of minimal length.
  • Generalized Edge-Biconnected Network Problem: The subgraph is edge-biconnected, i.e. there exists at least two edge-disjoint paths between any two nodes.
Generalized Minimum Spanning Tree Example of a Generalized Minimum Spanning Tree:
The graph G = (V, E) with node set V partitioned into clusters V1, ... , V5. The nodes p1, ... , p5 are selected from each cluster yielding the spanning tree of minimal costs.

Survivable Network Design

As part of the FHplus Project NETQUEST we consider extensions of the classical (Prize Collecting) Steiner Tree Problem to compute optimized and simulated cable-layings for access networks on the last mile. Depending on the variant, the goal is either to connect all customer nodes by a minimum cost subgraph (Operative Planning Task - OPT) or to maximize the estimated profit in the prize collecting version (Strategic Simulation Task - SST).

Depending on the concrete real life scenario we currently consider the following technical constraints:

  • Junction constraints: Attaching new connections to the existing infrastructure is allowed only at predefined junction nodes.
  • Non-Crossing constraints: Cable routes are not allowed to cross each other in a geometric sense.
  • Biconnectivity constraints: A subset of all customer nodes (type two customers) need to be redundantly connected by two node-disjoint paths.
  • bmax - Redundancy: Relaxing the biconnectivity constraints a type two customer may be connected to a biconnected node by a single branch line whichs length does not exceed bmax.

Biconnectivity Augmentation Problems

Biconnectivity Augmentation
Problems

A given network needs to be augmented by a cheapest possible subset C of additional edges so that any removal of a single node (edge) does not disconnect the graph.

Problems related to the Prize Collecting Steiner Tree Problem

Suppose that in our graph G each node v from V has associated a nonnegative prize p(v). In the prize collecting Steiner tree problem (PCST) the main goal is to find a subtree which minimizes the sum of costs of its edges plus the prizes (penalties) of nodes not spanned in that tree. In related problems, we are given an additional source node s from V. The goal is to find a subset C of edges representing a tree which spans optimal subset of nodes K*, so that the profit is maximized. We consider different objective functions based on the idea of PCST which can describe this profit.

See also our library of problem instances for the prize collecting Steiner tree problem.


These topics covered by our research are of great interest for different industrial fields. For example, energy providers are faced with a problem related to PCST: Minimize the planning, building and operating expenses while putting down a new district heating network which connects profitable costumers. Our work related to PCST represents a cooperation with industry.

Some of our Recent Publications Related to Network Design

2014:

  1. G. Fritz.
    A hybrid algorithm for the partition coloring problem.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, January 2014.
    supervised by G. Raidl and B. Hu.
    (PDF-file).

  2. L. Gouveia, P. Moura, M. Ruthmair, and A. Sousa.
    Spanning Trees with Variable Degree Bounds.
    Technical Report TR 186-1-14-02, Vienna University of Technology, Vienna, Austria, 2014.
    submitted to a journal.
    (PDF-file).

  3. J. Inführ and G. Raidl.
    A Memetic Algorithm for the Virtual Network Mapping Problem.
    Technical Report TR 186-1-14-01, Vienna University of Technology, Vienna, Austria, 2014.
    submitted to a journal.
    (PDF-file).

2013:

  1. L. Gouveia, M. Leitner, and I. Ljubic.
    Hop constrained steiner trees with multiple root nodes.
    Technical Report TR 186-1-13-02, Institute of Computer Graphics and Algorithms, Vienna University of Technology, 2013.
    submitted to a journal.
    (PDF-file).

  2. G. Hiermann, M. Prandtstetter, A. Rendl, J. Puchinger, and G. R. Raidl.
    Metaheuristics for solving a multimodal home-healthcare scheduling problem.
    Central European Journal of Operations Research, 2013.
    DOI:10.1007/s10100-013-0305-8.
    (a previous technical report version: PDF-file).

  3. J. Inführ.
    Optimization Challenges of the Future Federated Internet.
    PhD thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, Vienna, Austria, Oct. 2013.
    supervised by G. R. Raidl and K. Tutschku.
    (PDF-file).

  4. J. Inführ and G. R. Raidl.
    GRASP and variable neighborhood search for the virtual network mapping problem.
    In M. J. Blesa et al., editors, Hybrid Metaheuristics, 8th Int. Workshop, HM 2013, volume 7919 of LNCS, pages 159-173. Springer, 2013.
    (PDF-file).

  5. J. Inführ and G. R. Raidl.
    A memetic algorithm for the virtual network mapping problem.
    In H. Lau, P. Van Hentenryck, and G. Raidl, editors, Proceedings of the 10th Metaheuristics International Conference, pages 28/1-28/10, Singapore, 2013.
    (PDF-file).

  6. J. Inführ and G. R. Raidl.
    Solving the virtual network mapping problem with construction heuristics, local search and variable neighborhood descent.
    In M. Middendorf and C. Blum, editors, Evolutionary Computation in Combinatorial Optimisation - 13th European Conference, EvoCOP 2013, volume 7832 of LNCS, pages 250-261. Springer, 2013.
    (PDF-file).

  7. J. Inführ, D. Stezenbach, M. Hartmann, K. Tutschku, and G. R. Raidl.
    Using optimized virtual network embedding for network dimensioning.
    In Proceedings of Networked Systems 2013, pages 118-125, Stuttgart, Germany, 2013. IEEE.
    In print, preprint available at (PDF-file).

  8. P. Jahrmann and G. R. Raidl.
    Clique and independent set based grasp approaches for the regenerator location problem.
    In H. Lau, P. Van Hentenryck, and G. Raidl, editors, Proceedings of the 10th Metaheuristics International Conference, pages 30/1-30/10, Singapore, 2013.
    (PDF-file).

  9. R. Karl.
    The rooted delay-constrained steiner tree problem with uncertain delays.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, December 2013.
    supervised by G. Raidl, M. Leitner, and M. Ruthmair.
    (PDF-file).

  10. M. Leitner, M. Ruthmair, and G. R. Raidl.
    Stabilizing branch-and-price for constrained tree problems.
    Networks, 61(2):150-170, 2013.
    (a previous technical report version: PDF-file).

  11. P. C. Pop, B. Hu, and G. R. Raidl.
    A memetic algorithm for the partition graph coloring problem.
    In Extended Abstracts of the 14th International Conference on Computer Aided Systems Theory, pages 167-169, Las Palmas de Gran Canaria, Spain, 2013.
    (PDF-file).

  12. P. C. Pop, B. Hu, and G. R. Raidl.
    A memetic algorithm with two distinct solution representations for the partition graph coloring problem.
    In R. Moreno-Díaz, F. Pichler, and A. Quesada-Arencibia, editors, Computer Aided Systems Theory - EUROCAST 2013, volume 8111 of LNCS, pages 219-226. Springer, 2013.
    (PDF-file).

  13. T. Schnabl.
    Critical links detection using CUDA.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, April 2013.
    supervised by G. Raidl and B. Hu.
    (PDF-file).

  14. A. Schöbel, G. R. Raidl, I. Grujicic, G. Besau, and G. Schuster.
    An optimization model for integrated timetable based design of railway infrastructure.
    In Proceedings of the 5th International Seminar on Railway Operations Modelling and Analysis - RailCopenhagen 2013, pages 765-774. IAROR, 2013.
    (PDF-file).

  15. C.-D. Volko.
    Selective graph coloring problem.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, April 2013.
    supervised by G. Raidl and B. Hu.
    (PDF-file).

2012:

  1. M. Berlakovich, M. Ruthmair, and G. R. Raidl.
    A Multilevel Heuristic for the Rooted Delay-Constrained Minimum Spanning Tree Problem.
    In R. Moreno-Díaz, F. Pichler, and A. Quesada-Arencibia, editors, Proceedings of the 13th International Conference on Computer Aided Systems Theory: Part I, volume 6927 of LNCS, pages 256-263. Springer, 2012.
    (PDF-file).

  2. L. Gouveia, M. Leitner, and I. Ljubic.
    On the hop constrained Steiner tree problem with multiple root nodes.
    In A. Mahjoub et al., editors, Proceedings of the 2nd International Symposium on Combinatorial Optimization, volume 7422 of LNCS, pages 201-212. Springer, 2012.
    The original publication is available at www.springerlink.com, preprint as (PDF-file).

  3. L. Gouveia, M. Leitner, and I. Ljubic.
    The two-level diameter constrained spanning tree problem.
    Technical Report TR 186-1-12-02, Institute of Computer Graphics and Algorithms, Vienna University of Technology, 2012.
    submitted to a journal.
    (PDF-file).

  4. B. Hu and G. R. Raidl.
    An evolutionary algorithm with solution archive for the generalized minimum spanning tree problem.
    In R. Moreno-Díaz, F. Pichler, and A. Quesada-Arencibia, editors, Proceedings of the 13th International Conference on Computer Aided Systems Theory: Part I, volume 6927 of LNCS, pages 287-294. Springer, 2012.
    (PDF-file).

  5. B. Hu and G. R. Raidl.
    An evolutionary algorithm with solution archives and bounding extension for the generalized minimum spanning tree problem.
    In Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation (GECCO), pages 393-400, Philadelphia, PA, USA, 2012. ACM Press.
    (PDF-file).

  6. J. Inführ and G. Raidl.
    Automatic generation of 2-antwars players with genetic programming.
    In R. Moreno-Díaz, F. Pichler, and A. Quesada-Arencibia, editors, Computer Aided Systems Theory – EUROCAST 2011, volume 6927 of Lecture Notes in Computer Science, pages 248-255. Springer Berlin / Heidelberg, 2012.
    10.1007/978-3-642-27549-4_32.
    The original publication is available at www.springerlink.com.

  7. M. Leitner and G. R. Raidl.
    Variable neighborhood and greedy randomized adaptive search for capacitated connected facility location.
    In R. Moreno-Díaz et al., editors, Computer Aided Systems Theory – EUROCAST 2011, volume 6927 of LNCS, pages 295-302. Springer, 2012.
    The original publication is available at www.springerlink.com, preprint as (PDF-file).

  8. A. Rendl, M. Prandtstetter, G. Hiermann, J. Puchinger, and G. R. Raidl.
    Hybrid heuristics for multimodal homecare scheduling.
    In N. Beldiceanu, N. Jussien, and E. Pinson, editors, 9th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CPAIOR'12), volume 7298, pages 339-355. Springer, 2012.
    (PDF-file).

  9. M. Ruthmair.
    On Solving Constrained Tree Problems and an Adaptive Layers Framework.
    PhD thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, Vienna, Austria, May 2012.
    supervised by G. R. Raidl and U. Pferschy.
    (PDF-file).

  10. M. Ruthmair and G. R. Raidl.
    A Memetic Algorithm and a Solution Archive for the Rooted Delay-Constrained Minimum Spanning Tree Problem.
    In R. Moreno-Díaz et al., editors, Proceedings of the 13th International Conference on Computer Aided Systems Theory: Part I, volume 6927 of LNCS, pages 351-358. Springer, 2012.
    (PDF-file).

  11. M. Ruthmair and G. R. Raidl.
    On Solving the Rooted Delay- and Delay-Variation-Constrained Steiner Tree Problem.
    In A. Mahjoub et al., editors, Proceedings of the 2nd International Symposium on Combinatorial Optimization, volume 7422 of LNCS, pages 225-236. Springer, 2012.
    (PDF-file).

  12. C. Schauer and G. R. Raidl.
    Variable neighborhood search and GRASP for three-layer hierarchical ring network design.
    In C. A. C. Coello et al., editors, Parallel Problem Solving from Nature-PPSN XII, volume 7492 of LNCS, pages 458-467. Springer, 2012.
    (PDF-file).

2011:

  1. M. Berlakovich, M. Ruthmair, and G. R. Raidl.
    A Multilevel Heuristic for the Rooted Delay-Constrained Minimum Spanning Tree Problem.
    In A. Quesada-Arencibia et al., editors, Extended Abstracts of EUROCAST 2011 - 13th International Conference on Computer Aided Systems Theory, pages 247-249, Las Palmas de Gran Canaria, Spain, 2011.
    (PDF-file).

  2. A. Chwatal and G. R. Raidl.
    Solving the minimum label spanning tree problem by mathematical programming techniques.
    Advances in Operations Research, 2011(143732):1-38, 2011.
    (Download from Hindawi, PDF-file).

  3. G. Fritz.
    Heuristic methods for the hop constrained survivable network design problem.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, Vienna, Austria, September 2011.
    supervised by G. Raidl and M. Leitner.
    (PDF-file).

  4. C. Gruber.
    Ein Lösungsarchiv mit Branch-and-Bound-Erweiterung für das Generalized Minimum Spanning Tree Problem.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, September 2011.
    supervised by G. Raidl and B. Hu.
    (PDF-file).

  5. B. Hu and G. R. Raidl.
    An evolutionary algorithm with solution archive for the generalized minimum spanning tree problem.
    In A. Quesada-Arencibia et al., editors, Extended Abstracts of the 13th International Conference on Computer Aided Systems Theory, LNCS, pages 256-259, Las Palmas de Gran Canaria, Spain, 2011. Springer.
    (PDF-file).

  6. J. Inführ and G. R. Raidl.
    Introducing the virtual network mapping problem with delay, routing and location constraints.
    In J. Pahl, T. Reiners, and S. Voß, editors, Network Optimization: 5th International Conference, INOC 2011, volume 6701 of LNCS, pages 105-117, Hamburg, Germany, June 2011. Springer.
    The original publication is available at www.springerlink.com, preprint as (PDF-file).

  7. M. Leitner and G. R. Raidl.
    Branch-and-cut-and-price for capacitated connected facility location.
    Journal of Mathematical Modelling and Algorithms, 10(3):245-267, 2011.
    (a previous technical report version: PDF-file).

  8. M. Leitner and G. R. Raidl.
    Variable neighborhood search for capacitated connected facility location.
    In A. Quesada-Arencibia et al., editors, Extended Abstracts of the Thirteenth International Conference on Computer Aided Systems Theory, pages 261-263, 2011.
    (PDF-file).

  9. M. Leitner, M. Ruthmair, and G. R. Raidl.
    On stabilized branch-and-price for constrained tree problems.
    Technical Report TR 186-1-11-01, Vienna University of Technology, Vienna, Austria, 2011.
    submitted to a journal.
    (PDF-file).

  10. M. Leitner, M. Ruthmair, and G. R. Raidl.
    Stabilized branch-and-price for the rooted delay-constrained Steiner tree problem.
    In J. Pahl, T. Reiners, and S. Voß, editors, Network Optimization: 5th International Conference, INOC 2011, volume 6701 of LNCS, pages 124-138, Hamburg, Germany, June 2011. Springer.
    The original publication is available at www.springerlink.com, preprint as (PDF-file).

  11. M. Leitner, M. Ruthmair, and G. R. Raidl.
    Stabilized column generation for the rooted delay-constrained Steiner tree problem.
    In Proceedings of the VII ALIO/EURO – Workshop on Applied Combinatorial Optimization, pages 250-253, Porto, Portugal, May 2011.
    (PDF-file).

  12. M. Ruthmair, A. Hubmer, and G. R. Raidl.
    Using a Solution Archive to Enhance Metaheuristics for the Rooted Delay-Constrained Minimum Spanning Tree Problem.
    In A. Quesada-Arencibia et al., editors, Extended Abstracts of EUROCAST 2011 - 13th International Conference on Computer Aided Systems Theory, pages 285-287, Las Palmas de Gran Canaria, Spain, 2011.
    (PDF-file).

  13. M. Ruthmair and G. R. Raidl.
    A Layered Graph Model and an Adaptive Layers Framework to Solve Delay-Constrained Minimum Tree Problems.
    In O. Günlük and G. Woeginger, editors, Fifteenth Conference on Integer Programming and Combinatorial Optimization (IPCO XV), volume 6655 of LNCS, pages 376-388. Springer, Heidelberg, 2011.
    (PDF-file).

  14. T. Seidl.
    A Multilevel Refinement Approach to the Rooted Delay-Constrained Steiner Tree Problem.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, Vienna, Austria, September 2011.
    supervised by G. Raidl and M. Ruthmair.
    (PDF-file).

  15. M. Sinnl.
    Branch-and-price for the Steiner tree problem with revenues, budget and hop constraints.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, Vienna, Austria, August 2011.
    supervised by G. Raidl and M. Leitner.
    (PDF-file).

2010:

  1. M. Berlakovich.
    Multilevel Heuristiken für das Rooted Delay-Constrained Minimum Spanning Tree Problem.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, Vienna, Austria, July 2010.
    supervised by G. Raidl and M. Ruthmair.
    (PDF-file).

  2. A. M. Chwatal.
    On the Minimum Label Spanning Tree Problem: Solution Methods and Applications.
    PhD thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, Vienna, Austria, May 2010.
    supervised by G. R. Raidl and U. Pferschy.
    (PDF-file).

  3. B. Hu, M. Leitner, and G. R. Raidl.
    The generalized minimum edge biconnected network problem: Efficient neighborhood structures for variable neighborhood search.
    Networks, 55(3):257-275, 2010.
    (a previous technical report version: PDF-file).

  4. M. Leitner.
    Solving Two Network Design Problems by Mixed Integer Programming and Hybrid Optimization Methods.
    PhD thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, Vienna, Austria, May 2010.
    supervised by G. R. Raidl and U. Pferschy.
    (PDF-file).

  5. M. Leitner and G. R. Raidl.
    Branch-and-cut-and-price for capacitated connected facility location.
    Technical Report TR 186-1-10-01, Vienna University of Technology, Vienna, Austria, 2010.
    (PDF-file).

  6. M. Leitner and G. R. Raidl.
    Strong lower bounds for a survivable network design problem.
    In M. Haouari and A. R. Mahjoub, editors, ISCO 2010 - International Symposium on Combinatorial Optimization, volume 36 of ENDM, pages 295-302. Elsevier, August 2010.
    preprint as (PDF-file).

  7. M. Leitner, G. R. Raidl, and U. Pferschy.
    Branch-and-price for a survivable network design problem.
    Technical Report TR 186-1-10-02, Vienna University of Technology, Vienna, Austria, 2010.
    submitted to a journal.
    (PDF-file).

  8. K. Oberlechner.
    Solving the k-node minimum label spanning arborescence problem with exact and heuristic methods.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, August 2010.
    supervised by G. Raidl and A. Chwatal.
    (PDF-file).

  9. A. Pagacz.
    Heuristic methods for solving two generalized network problems.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, February 2010.
    supervised by G. Raidl and B. Hu.
    (PDF-file).

  10. A. Pagacz, B. Hu, and G. R. Raidl.
    A memetic algorithm with population management for the generalized minimum vertex-biconnected network problem.
    In F. Xhafa et al., editors, 2nd International Conference on Intelligent Networking and Collaborative Systems, Workshop on Information Network Design, pages 356-361. Conference Publishing Services, 2010.
    (PDF-file).

  11. S. Pirkwieser and G. R. Raidl.
    Multilevel variable neighborhood search for periodic routing problems.
    In P. Cowling and P. Merz, editors, Evolutionary Computation in Combinatorial Optimisation - EvoCOP 2010, volume 6022 of LNCS, pages 226-238. Springer, 2010.
    (PDF-file).

  12. G. R. Raidl and B. Hu.
    Enhancing genetic algorithms by a trie-based complete solution archive.
    In P. Cowling and P. Merz, editors, Evolutionary Computation in Combinatorial Optimisation - EvoCOP 2010, volume 6022 of LNCS, pages 239-251. Springer, 2010.
    best paper award winner.
    (PDF-file).

  13. M. Ruthmair and G. R. Raidl.
    Variable neighborhood search and ant colony optimization for the rooted delay-constrained minimum spanning tree problem.
    In R. Schaefer et al., editors, Parallel Problem Solving from Nature - PPSN XI, Part II, volume 6239 of LNCS, pages 391-400. Springer, 2010.
    (PDF-file).

  14. M. Sonnleitner.
    Ein neues Lösungsarchiv für das Generalized Minimum Spanning Tree-Problem.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, September 2010.
    supervised by G. Raidl and B. Hu.
    (PDF-file).

  15. C. Thöni.
    Compressing fingerprint templates by solving the $k$-node minimum label spanning arborescence problem by branch-and-price.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, February 2010.
    supervised by G. Raidl and A. Chwatal.
    (PDF-file).

2009:

  1. A. M. Chwatal, N. Musil, and G. R. Raidl.
    Solving a multi-constrained network design problem by lagrangean decomposition and column generation.
    In M. G. S. et al., editor, Proceedings of the International Network Optimization Conference 2009, Pisa, Italy, April 2009.
    International Network Optimization Conference 2009.
    (PDF-file).

  2. A. M. Chwatal, G. R. Raidl, and K. Oberlechner.
    Solving a $k$-node minimum label spanning arborescence problem to compress fingerprint templates.
    Journal of Mathematical Modelling and Algorithms, 8:293-334, 2009.
    (Springer).

  3. M. Gruber.
    Exact and Heuristic Approaches for Solving the Bounded Diameter Minimum Spanning Tree Problem.
    PhD thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, May 2009.
    supervised by G. Raidl.
    (PDF-file).

  4. M. Gruber and G. R. Raidl.
    Cluster-based (meta-)heuristics for the Euclidean bounded diameter minimum spanning tree problem.
    In A. Quesada-Arencibia et al., editors, Extended Abstracts of the Twelfth International Conference on Computer Aided Systems Theory (EUROCAST 2009), pages 228-231, Gran Canaria, Spain, 2009.
    (PDF-file).

  5. M. Gruber and G. R. Raidl.
    Exploiting hierarchical clustering for finding bounded diameter minimum spanning trees on Euclidean instances.
    In G. R. Raidl et al., editors, Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation (GECCO), pages 263-270, Montréal, Québec, Canada, 2009. ACM New York, NY, USA.
    (PDF-file).

  6. M. Gruber and G. R. Raidl.
    (Meta-)heuristic separation of jump cuts in a branch&cut approach for the bounded diameter minimum spanning tree problem.
    In V. Maniezzo, T. Stützle, and S. Voss, editors, Matheuristics - Hybridizing Metaheuristics and Mathematical Programming, volume 10 of Annals of Information Systems, pages 209-230. Springer, 2009.
    (PDF-file).

  7. M. Gruber and G. R. Raidl.
    Solving the Euclidean bounded diameter minimum spanning tree problem by clustering-based (meta-)heuristics.
    In R. Moreno-Díaz et al., editors, Twelfth International Conference on Computer Aided Systems Theory (EUROCAST 2009), volume 5717 of LNCS, pages 665-672, Gran Canaria, Spain, 2009. Springer.
    (PDF-file).

  8. M. Gruber and G. R. Raidl.
    (Meta-)heuristic separation of jump cuts in a branch&cut approach for the bounded diameter minimum spanning tree problem, to appear 2009.
    special issue on Matheuristics of Operations Research / Computer Science Interface Series, Springer.
    (PDF-file).

  9. B. Hu and G. R. Raidl.
    A memetic algorithm for the generalized minimum vertex-biconnected network problem.
    In 9th International Conference on Hybrid Intelligent Systems - HIS 2009, pages 63-68, Shenyang, China, 2009.
    best paper award winner.
    (PDF-file).

  10. M. Leitner and G. R. Raidl.
    Combining lagrangian decomposition with very large scale neighborhood search for capacitated connected facility location.
    Technical Report TR 186-1-09-02, Institute of Computer Graphics and Algorithms, Vienna University of Technology, 2009.
    (PDF-file).

  11. M. Leitner and G. R. Raidl.
    A lagrangian decomposition based heuristic for capacitated connected facility location.
    In S. Voss and M. Caserta, editors, Proceedings of the 8th Metaheuristic International Conference (MIC 2009), Hamburg, Germany, July 2009.
    (PDF-file).

  12. M. Leitner, G. R. Raidl, and U. Pferschy.
    Accelerating column generation for a survivable network design problem.
    In M. G. Scutellà et al., editors, Proceedings of the International Network Optimization Conference 2009, Pisa, Italy, April 2009.
    (PDF-file).

  13. H. Obweger.
    Similarity searching in complex business events and sequences thereof.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphic s and Algorithms, Vienna, Austria, March 2009.
    supervised by G. Raidl.
    (PDF-file).

  14. S. Pirkwieser and G. R. Raidl.
    A column generation approach for the periodic vehicle routing problem with time windows.
    In M. G. Scutellà et al., editors, Proceedings of the International Network Optimization Conference 2009, Pisa, Italy, 26-29 Apr. 2009.
    (PDF-file).

  15. M. Prandtstetter.
    Hybrid Optimization Methods for Warehouse Logistics and the Reconstruction of Destroyed Paper Documents.
    PhD thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, December 2009.
    supervised by G. Raidl.
    (PDF-file).

  16. M. Ruthmair and G. R. Raidl.
    A Kruskal-Based Heuristic for the Rooted Delay-Constrained Minimum Spanning Tree Problem.
    In R. Moreno-Díaz et al., editors, Computer Aided Systems Theory - EUROCAST 2009, volume 5717 of LNCS, pages 713-720. Springer, 2009.
    (PDF-file).

  17. M. Ruthmair and G. R. Raidl.
    A Kruskal-based heuristic for the rooted delay-constrained minimum spanning tree problem.
    In A. Quesada-Arencibia et al., editors, Extended Abstracts of the Twelfth International Conference on Computer Aided Systems Theory (EUROCAST 2009), pages 244-246, Gran Canaria, Spain, 2009.
    (PDF-file).

  18. A. Sramko.
    Enhancing a genetic algorithm by a complete solution archive based on a trie data structure.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphic s and Algorithms, Vienna, Austria, February 2009.
    supervised by G. Raidl.
    (PDF-file).

  19. M. Suntinger.
    Event based similarity search and its applications in business analytics.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphic s and Algorithms, Vienna, Austria, March 2009.
    supervised by G. Raidl.
    (PDF-file).

  20. J. Walla.
    Exakte und heuristische Optimierungsmethoden zur Lösung von Video Server Load Re-Balancing.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, Vienna, Austria, April 2009.
    supervised by G. Raidl and M. Ruthmair.
    (PDF-file).

  21. M. Wolf.
    Ein Lösungsarchiv-unterstützter evolutionärer Algorithmus für das Generalized Minimum Spanning Tree-Problem.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, July 2009.
    supervised by G. Raidl and B. Hu.
    (PDF-file).

2008:

  1. O. Dietzel.
    Combinatorial Optimization for the Compression of Biometric Templates.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, May 2008.
    supervised by G. Raidl and A. Chwatal.
    (PDF-file).

  2. M. Gruber and G. R. Raidl.
    Heuristic cut separation in a branch&cut approach for the bounded diameter minimum spanning tree problem.
    In Proceedings of the 2008 International Symposium on Applications and the Internet, SAINT 2008, pages 261-264, Turku, Finland, 2008. IEEE Computer Society.
    (PDF-file).

  3. M. Gruber and G. R. Raidl.
    (Meta-)heuristic separation of jump cuts for the bounded diameter minimum spanning tree problem.
    In P. Hansen et al., editors, Proceedings of Matheuristics 2008: Second International Workshop on Model Based Metaheuristics, Bertinoro, Italy, 2008.
    (PDF-file).

  4. M. Gruber and G. R. Raidl.
    (Meta-)heuristic separation of jump cuts in a branch&cut approach for the bounded diameter minimum spanning tree problem, to appear 2009.
    special issue on Matheuristics of Operations Research / Computer Science Interface Series, Springer.
    (PDF-file).

  5. B. Hu.
    Hybrid Metaheuristics for Generalized Network Design Problems.
    PhD thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, Vienna, Austria, December 2008.
    supervised by G. R. Raidl and U. Pferschy.
    (PDF-file).

  6. B. Hu, M. Leitner, and G. R. Raidl.
    Combining variable neighborhood search with integer linear programming for the generalized minimum spanning tree problem.
    Journal of Heuristics, 14(5):473-499, 2008.
    (a previous technical report version: PDF-file).

  7. B. Hu and G. R. Raidl.
    Effective neighborhood structures for the generalized traveling salesman problem.
    In J. van Hemert and C. Cotta, editors, Evolutionary Computation in Combinatorial Optimisation - EvoCOP 2008, volume 4972 of LNCS, pages 36-47, Naples, Italy, 2008. Springer.
    best paper award winner.
    (PDF-file).

  8. B. Hu and G. R. Raidl.
    Solving the railway traveling salesman problem via a transformation into the classical traveling salesman problem.
    In F. Xhafa, F. Herrera, A. Abraham, M. Köppen, and J. M. Benitez, editors, 8th International Conference on Hybrid Intelligent Systems - HIS 2008, pages 73-77, Barcelona, Spain, 2008.
    (PDF-file).

  9. M. Leitner and G. R. Raidl.
    Lagrangian decomposition, metaheuristics, and hybrid approaches for the design of the last mile in fiber optic networks.
    In M. J. Blesa et al., editors, Hybrid Metaheuristics 2008, volume 5296 of LNCS, pages 158-174, Malaga, Spain, October 2008. Springer-Verlag Berlin Heidelberg.
    (SpringerLink, preprint as PDF-file).

  10. M. Leitner and G. R. Raidl.
    Variable neighborhood search for a prize collecting capacity constrained connected facility location problem.
    In Proceedings of the 2008 International Symposium on Applications and the Internet, SAINT 2008, pages 233-236, Turku, Finland, 2008. IEEE Computer Society.
    (PDF-file).

  11. N. Musil.
    Design eines sicherheits-, zeit- und kostenkritischen Kommunikationsnetzwerkes mittels Lagrange Relaxierung und Spaltengenerierung.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, December 2008.
    supervised by G. Raidl and A. Chwatal.
    (PDF-file).

  12. S. Pirkwieser, G. R. Raidl, and J. Puchinger.
    A Lagrangian decomposition/evolutionary algorithm hybrid for the knapsack constrained maximum spanning tree problem.
    In C. Cotta and J. van Hemert, editors, Recent Advances in Evolutionary Computation for Combinatorial Optimization, volume 153 of Studies in Computational Intelligence, pages 69-85. Springer, 2008.
    (Book Website, a previous technical report version: PDF-file).

  13. G. R. Raidl and M. Gruber.
    A Lagrangian relax-and-cut approach for the bounded diameter minimum spanning tree problem.
    In T. E. Simos et al., editors, Numerical Analysis and Applied Mathematics, volume 1048 of AIP Conference Proceedings, pages 446-449. American Institute of Physics, 2008.
    (PDF-file).

  14. F. Zaubzer.
    Lagrangian relax-and-cut and hybrid methods for the bounded diameter and the hop constrained minimum spanning tree problems.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, May 2008.
    supervised by G. Raidl and M. Gruber.
    (PDF-file).

2007:

  1. A. Braumann.
    Map-Matching und Wegsuche in einem geografischen Informationssystem.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, Vienna, Austria, January 2007.
    supervised by G. Raidl.

  2. T. Bucsics.
    Metaheuristic approaches for designing survivable fiber-optic networks.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, Vienna, Austria, March 2007.
    supervised by G. Raidl and D. Wagner.
    (PDF-file).

  3. A. Chwatal, G. R. Raidl, and O. Dietzel.
    Compressing fingerprint templates by solving an extended minimum label spanning tree problem.
    In Proceedings of MIC2007, the 7th Metaheuristics International Conference, pages 105/1-3, Montreal, Canada, 2007.
    (PDF-file).

  4. B. Hu, M. Leitner, and G. R. Raidl.
    The generalized minimum edge biconnected network problem: Efficient neighborhood structures for variable neighborhood search.
    Technical Report TR 186-1-07-02, Institute of Computer Graphics and Algorithms, Vienna University of Technology, 2007.
    submitted to Networks.
    (PDF-file).

  5. M. Leitner, B. Hu, and G. R. Raidl.
    Variable neighborhood search for the generalized minimum edge biconnected network problem.
    In B. Fortz, editor, Proceedings of the International Network Optimization Conference 2007, pages 69/1-6, Spa, Belgium, 2007.
    (PDF-file).

  6. I. Ljubic.
    A hybrid VNS for connected facility location.
    In Proceedings of Hybrid Metaheuristics, HM 2007. Springer, 2007.
    to appear.
    (PDF-file).

  7. S. Pirkwieser, G. R. Raidl, and J. Puchinger.
    Combining Lagrangian decomposition with an evolutionary algorithm for the knapsack constrained maximum spanning tree problem.
    In C. Cotta and J. van Hemert, editors, Evolutionary Computation in Combinatorial Optimisation - EvoCOP 2007, volume 4446 of LNCS, pages 176-187. Springer, 2007.
    (PDF-file).

  8. S. Pirkwieser, G. R. Raidl, and J. Puchinger.
    A Lagrangian decomposition/evolutionary algorithm hybrid for the knapsack constrained maximum spanning tree problem.
    Technical Report TR 186-1-07-03, Institute of Computer Graphics and Algorithms, Vienna University of Technology, 2007.
    accepted for publication in Springer Studies in Computational Intelligence.
    (PDF-file).

  9. P. Putz.
    Subgradient optimization based lagrangian relaxation and relax-and-cut approaches for the bounded-diameter minimum spanning tree problem.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, October 2007.
    supervised by G. Raidl and M. Gruber.
    (PDF-file).

  10. G. R. Raidl and A. Chwatal.
    Fingerprint template compression by solving a minimum label $k$-node subtree problem.
    In E. Simos, editor, Numerical Analysis and Applied Mathematics, volume 936 of AIP Conference Proceedings, pages 444-447. American Institute of Physics, 2007.
    (PDF-file).

  11. D. Wagner, G. R. Raidl, U. Pferschy, P. Mutzel, and P. Bachhiesl.
    A multi-commodity flow approach for the design of the last mile in real-world fiber optic networks.
    In K.-H. Waldmann and U. M. Stocker, editors, Operations Research Proceedings 2006. Springer, 2007.
    (PDF-file).

  12. D. Wanger, U. Pferschy, P. Mutzel, G. R. Raidl, and P.Bachhiesl.
    A directed cut model for the design of the last mile in real-world fiber optic networks.
    In B. Fortz, editor, Proceedings of the International Network Optimization Conference 2007, pages 103/1-6, Spa, Belgium, 2007.
    (PDF-file).

2006:

  1. R. Beibl.
    Cluster planarity testing for the case of not necessarily connected clusters.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, Vienna, Austria, December 2006.
    supervised by G. Raidl, R. Weiskircher, and M. Percan.
    (PDF-file).

  2. M. Gruber, J. van Hemert, and G. R. Raidl.
    Neighborhood searches for the bounded diameter minimum spanning tree problem embedded in a VNS, EA, and ACO.
    In M. Keijzer et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO 2006, volume 2, pages 1187-1194, Seattle, USA, 2006. ACM.
    (PDF-file).

  3. B. Hu, M. Leitner, and G. R. Raidl.
    Combining variable neighborhood search with integer linear programming for the generalized minimum spanning tree problem.
    Technical Report TR 186-1-06-01, Institute of Computer Graphics and Algorithms, Vienna University of Technology, 2006.
    submitted to Journal of Heuristics.
    (PDF-file).

  4. B. Hu and G. R. Raidl.
    Variable neighborhood descent with self-adaptive neighborhood-ordering.
    In C. Cotta, A. J. Fernandez, and J. E. Gallardo, editors, Proceedings of the 7th EU/MEeting on Adaptive, Self-Adaptive, and Multi-Level Metaheuristics, Malaga, Spain, 2006.
    (PDF-file).

  5. B. Kopinitsch.
    An ant colony optimisation algorithm for the bounded diameter minimum spanning tree problem.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, January 2006.
    supervised by G. Raidl and M. Gruber.
    (PDF-file).

  6. M. Leitner.
    Solving two generalized network design problems with exact and heuristic methods.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, May 2006.
    supervised by G. Raidl and B. Hu.
    (PDF-file).

  7. I. Ljubic, R. Weiskircher, U. Pferschy, G. Klau, P. Mutzel, and M. Fischetti.
    An algorithmic framework for the exact solution of the prize-collecting Steiner tree problem.
    Mathematical Programming, Series B, 105(2-3):427-449, 2006.
    Previous technical report as (PDF-file).

  8. A. Pagacz, G. R. Raidl, and S. Zawislak.
    Evolutionary approach to constrained minimum spanning tree problem.
    In J. Arabasa, editor, Evolutionary Computation and Global Optimization 2006, pages 331-341, Murzasichle, Poland, 2006.
    (PDF-file).

  9. S. Pirkwieser.
    A Lagrangian decomposition approach combined with metaheuristics for the knapsack constrained maximum spanning tree problem.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, October 2006.
    supervised by G. Raidl and J. Puchinger.
    (PDF-file).

  10. G. R. Raidl, G. Koller, and B. A. Julstrom.
    Biased mutation operators for subgraph-selection problems.
    IEEE Transactions on Evolutionary Computation, to appear 2005.
    (a previous technical report version: PDF-file).

2005:

  1. M. Gruber and G. R. Raidl.
    A new 0-1 ILP approach for the bounded diameter minimum spanning tree problem.
    In L. Gouveia and C. Mourão, editors, Proceedings of the 2nd International Network Optimization Conference, volume 1, pages 178-185, Lisbon, Portugal, 2005.
    (PDF-file).

  2. M. Gruber and G. R. Raidl.
    Variable neighborhood search for the bounded diameter minimum spanning tree problem.
    In P. Hansen, N. Mladenovic, J. A. M. Pérez, B. M. Batista, and J. M. Moreno-Vega, editors, Proceedings of the 18th Mini Euro Conference on Variable Neighborhood Search, Tenerife, Spain, 2005.
    (PDF-file).

  3. B. Hu, M. Leitner, and G. R. Raidl.
    Computing generalized minimum spanning trees with variable neighborhood search.
    In P. Hansen, N. Mladenovic, J. A. M. Pérez, B. M. Batista, and J. M. Moreno-Vega, editors, Proceedings of the 18th Mini Euro Conference on Variable Neighborhood Search, Tenerife, Spain, 2005.
    (PDF-file).

  4. I. Ljubic, R. Weiskircher, U. Pferschy, G. Klau, P. Mutzel, and M. Fischetti.
    Solving the prize-collecting Steiner tree problem to optimality.
    In C. Demetrescu, R. Tamassia, and R. Sedgewick, editors, Proceedings of ALENEX 2005, Seventh Workshop on Algorithm Engineering and Experiments, Vancouver, British Columbia, Canada, January 22, 2005. SIAM, 2005.
    ISBN 0-89871-596-2.
    (PDF-file).

  5. A. Moser.
    Finding provably optimal solutions for the (prize collecting) steiner tree problem.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, April 2005.
    supervised by P. Mutzel and I. Ljubic.
    (PDF-file).

  6. G. R. Raidl, G. Koller, and B. A. Julstrom.
    Biased mutation operators for subgraph-selection problems.
    IEEE Transactions on Evolutionary Computation, to appear 2005.
    (a previous technical report version: PDF-file).

2004:

  1. G. Gruber.
    Ein Genetischer Algorithmus für das Optimum Communication Spanning Tree Problem.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, Vienna, Austria, November 2004.
    supervised by G. Raidl.
    (PDF-file).

  2. G. W. Klau, I. Ljubic, A. Moser, P. Mutzel, P. Neuner, U. P. G. Raidl, and R. Weiskircher.
    Combining a memetic algorithm with integer programming to solve the prize-collecting Steiner tree problem.
    In K. Deb, R. Poli, W. Banzhaf, H.-G. Beyer, E. Burke, P. Darwen, D. Dasgupta, D. Floreano, J. Foster, M. Harman, O. Holland, P. Lanzi, L. Spector, A. Tettamanzi, D. Thierens, and A. Tyrrell, editors, Genetic and Evolutionary Computation - GECCO 2004, volume 3102 of LNCS, pages 1304-1315. Springer-Verlag, 2004.
    (PDF-file).

  3. I. Ljubic.
    Exact and Memetic Algorithms for Two Network Design Problems.
    PhD thesis, Faculty of Computer Science, Vienna University of Technology, November 2004.
    2.6MB (PDF-file).

  4. G. R. Raidl and H. Feltl.
    An improved hybrid genetic algorithm for the generalized assignment problem.
    In H. M. Haddadd et al., editors, Proceedings of the 2003 ACM Symposium on Applied Computing, pages 990-995. ACM Press, 2004.
    (PDF-file).

  5. G. R. Raidl, G. Koller, and B. A. Julstrom.
    Biased mutation operators for subgraph-selection problems.
    Technical Report TR 186-1-04-06, Institute of Computer Graphics and Algorithms, Vienna University of Technology, Vienna, Austria, 2004.
    submitted for publication in the IEEE Transactions on Evolutionary Computation.
    (PDF-file).

2003:

  1. B. A. Julstrom and G. R. Raidl.
    A permutation-coded evolutionary algorithm for the bounded-diameter minimum spanning tree problem.
    In A. Barry, F. Rothlauf, D. Thierens, et al., editors, in 2003 Genetic and Evolutionary Computation Conference's Workshops Proceedings, Workshop on Analysis and Design of Representations, pages 2-7, 2003.
    best paper award winner of the workshop.
    (PDF-file).

  2. S. A. Kersting.
    Ein evolutionärer Algorithmus zur Lösung des Vertex-Biconnectivity Augmentation Problems.
    Master's thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, Vienna, Austria, September 2003.
    supervised by G. Raidl.
    (PDF-file).

  3. G. Klau, I. Ljubic, P. Mutzel, U. Pferschy, and R. Weiskircher.
    The fractional prize-collecting Steiner tree problem on trees.
    In G. D. Battista and U. Zwick, editors, ESA 2003, volume 2832 of LNCS, pages 691-702. Springer-Verlag, 2003.
    (PDF-file).

  4. G. Klau, I. Ljubic, P. Mutzel, U. Pferschy, and R. Weiskircher.
    The fractional prize-collecting Steiner tree problem on trees.
    Technical Report TR-186-1-03-01, Institute of Computer Graphics and Algorithms, Vienna University of Technology, 2003.
    submitted to ESA.
    (PDF-file).

  5. I. Ljubic and G. R. Raidl.
    A memetic algorithm for minimum-cost vertex-biconnectivity augmentation of graphs.
    Journal of Heuristics, 9(5):401-428, 2003.
    Previous technical report as PDF-file.

  6. G. R. Raidl.
    Hybrid evolutionary algorithms for combinatorial optimization.
    Habilitation thesis at the Vienna University of Technology, March 2003.
    (PDF-version).

  7. G. R. Raidl and B. A. Julstrom.
    Edge-sets: An effective evolutionary coding of spanning trees.
    IEEE Transactions on Evolutionary Computation, 7(3):225-239, 2003.
    (a previous technical report version: PDF-file).

  8. G. R. Raidl and B. A. Julstrom.
    Greedy heuristics and an evolutionary algorithm for the bounded-diameter minimum spanning tree problem.
    In G. Lamont et al., editors, Proceedings of the 2003 ACM Symposium on Applied Computing, pages 747-752. ACM Press, 2003.
    (PDF-file).

2002:

  1. J. Hackner, G. Klau, G. Kodydek, I. Ljubic, A. Moser, P. Mutzel, P. Neuner, G. Raidl, M. Schönhacker, and R. Weiskircher.
    Ansätze zur räumlichen Ausbauoptimierung von Fernwärmenetzen (abstract).
    International Conference on Operations Research, OR 2002, Klagenfurt, Austria, 2002.

  2. B. A. Julstrom and G. R. Raidl.
    Initialization is robust in evolutionary algorithms that encode spanning trees as sets of edges.
    In G. Lamont et al., editors, Proceedings of the 2002 ACM Symposium on Applied Computing, pages 547-552. ACM Press, 2002.
    (PDF-file).

  3. S. Kersting, G. R. Raidl, and I. Ljubic.
    A memetic algorithm for vertex-biconnectivity augmentation.
    In S. Cagnoni et al., editors, Applications of Evolutionary Computing: EvoWorkshops 2002, volume 2279 of LNCS, pages 102-111. Springer, 2002.
    (PDF-file).

  4. I. Ljubic, P. Mutzel, and G. Raidl.
    Combining branch-and-cut-and-price with a memetic algorithm for biconnectivity augmentation (abstract).
    International Conference on Operations Research, OR 2002, Klagenfurt, Austria, 2002.

  5. G. R. Raidl, G. Kodydek, and B. Julstrom.
    On weight-biased mutation for graph problems.
    In J. J. M. Guervos, P. Adamidis, H.-G. Beyer, J.-L. Fernández-Villacañas, and H.-P. Schwefel, editors, Parallel Problem Solving from Nature - PPSN VII, volume 2439 of Lecture Notes in Computer Science, pages 204-213, Granada, Spain, September 2002. Springer-Verlag.
    (web page at Springer, PDF-file © Springer-Verlag).

  6. G. R. Raidl and I. Ljubic.
    Evolutionary local search for the edge-biconnectivity augmentation problem.
    Information Processing Letters, 82(1):39-45, 2002.
    (a previous technical report version: PDF-file).

2001:

  1. J. Gottlieb, B. A. Julstrom, F. Rothlauf, and G. R. Raidl.
    Prüfer numbers: A poor representation of spanning trees for evolutionary search.
    In L. Spector et al., editors, Proceedings of the 2001 Genetic and Evolutionary Computation Conference, pages 343-350. Morgan Kaufmann, 2001.
    (PDF-file).

  2. B. A. Julstrom and G. R. Raidl.
    Weight-biased edge-crossover in evolutionary algorithms for two graph problems.
    In G. Lamont et al., editors, Proceedings of the 16th ACM Symposium on Applied Computing, pages 321-326. ACM Press, 2001.
    (PDF-file).

  3. J. Kratica, D. Tošic, V. Filipovic, and I. Ljubic.
    A genetic algorithm for the uncapacitated network design problem.
    In R. Roy et al., editors, Soft Computing in Industry - Recent Applications, Engineering series, pages 329-338. Springer, 2001.

  4. I. Ljubic and G. R. Raidl.
    An evolutionary algorithm with hill-climbing for the edge-biconnectivity augmentation problem.
    In E. J.-W. Boers et al., editors, Applications of Evolutionary Computing: EvoWorkshops 2001, volume 2037 of LNCS, pages 20-29. Springer, 2001.
    (PDF-file).

2000:

  1. I. Ljubic and J. Kratica.
    A genetic algorithm for biconnectivity augmentation problem.
    In C. Fonseca et al., editors, Proceedings of the 2000 IEEE Congress on Evolutionary Computation, pages 89-96. IEEE Press, 2000.

  2. I. Ljubic, G. R. Raidl, and J. Kratica.
    A hybrid GA for the edge-biconnectivity augmentation problem.
    In K. Deb et al., editors, Parallel Problem Solving from Nature - PPSN VI, volume 1917 of LNCS, pages 641-650. Springer, 2000.
    (PDF-file).

  3. G. R. Raidl.
    An efficient evolutionary algorithm for the degree-constrained minimum spanning tree problem.
    In C. Fonseca et al., editors, Proceedings of the 2000 IEEE Congress on Evolutionary Computation, pages 104-111. IEEE Press, 2000.
    (PDF-file).

  4. G. R. Raidl and C. Drexel.
    A predecessor coding in an evolutionary algorithm for the capacitated minimum spanning tree problem.
    In Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference, pages 309-316, Las Vegas, NV, 2000.
    (PDF-file).

  5. G. R. Raidl and B. A. Julstrom.
    A weighted coding in a genetic algorithm for the degree-constrained minimum spanning tree problem.
    In J. Carroll et al., editors, Proceedings of the 2000 ACM Symposium on Applied Computing, pages 440-445. ACM Press, 2000.
    (PDF-file).

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