Evolutionary Computation in Combinatorial Optimization

1. Front Matter

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2. A Hyper-heuristic with a Round Robin Neighbourhood Selection

   • Ahmed Kheiri, Ender Özcan

   Pages 1-12

3. A Multiobjective Approach Based on the Law of Gravity and Mass Interactions for Optimizing Networks

   • Álvaro Rubio-Largo, Miguel A. Vega-Rodríguez

   Pages 13-24

4. A Multi-objective Feature Selection Approach Based on Binary PSO and Rough Set Theory

   • Liam Cervante, Bing Xue, Lin Shang, Mengjie Zhang

   Pages 25-36

5. A New Crossover for Solving Constraint Satisfaction Problems

   • Reza Abbasian, Malek Mouhoub

   Pages 37-48

6. A Population-Based Strategic Oscillation Algorithm for Linear Ordering Problem with Cumulative Costs

   • Wei Xiao, Wenqing Chu, Zhipeng Lü, Tao Ye, Guang Liu, Shanshan Cui

   Pages 49-60

7. A Study of Adaptive Perturbation Strategy for Iterated Local Search

   • Una Benlic, Jin-Kao Hao

   Pages 61-72

8. Adaptive MOEA/D for QoS-Based Web Service Composition

   • Mihai Suciu, Denis Pallez, Marcel Cremene, Dumitru Dumitrescu

   Pages 73-84

9. An Analysis of Local Search for the Bi-objective Bidimensional Knapsack Problem

   • Leonardo C. T. Bezerra, Manuel López-Ibáñez, Thomas Stützle

   Pages 85-96

10. An Artificial Immune System Based Approach for Solving the Nurse Re-rostering Problem

    • Broos Maenhout, Mario Vanhoucke

    Pages 97-108

11. Automatic Algorithm Selection for the Quadratic Assignment Problem Using Fitness Landscape Analysis

    • Erik Pitzer, Andreas Beham, Michael Affenzeller

    Pages 109-120

12. Balancing Bicycle Sharing Systems: A Variable Neighborhood Search Approach

    • Marian Rainer-Harbach, Petrina Papazek, Bin Hu, Günther R. Raidl

    Pages 121-132

13. Combinatorial Neighborhood Topology Particle Swarm Optimization Algorithm for the Vehicle Routing Problem

    • Yannis Marinakis, Magdalene Marinaki

    Pages 133-144

14. Dynamic Evolutionary Membrane Algorithm in Dynamic Environments

    • Chuang Liu, Min Han

    Pages 145-156

15. From Sequential to Parallel Local Search for SAT

    • Alejandro Arbelaez, Philippe Codognet

    Pages 157-168

16. Generalizing Hyper-heuristics via Apprenticeship Learning

    • Shahriar Asta, Ender Özcan, Andrew J. Parkes, A. Şima Etaner-Uyar

    Pages 169-178

17. High-Order Sequence Entropies for Measuring Population Diversity in the Traveling Salesman Problem

    • Yuichi Nagata, Isao Ono

    Pages 179-190

18. Investigating Monte-Carlo Methods on the Weak Schur Problem

    • Shalom Eliahou, Cyril Fonlupt, Jean Fromentin, Virginie Marion-Poty, Denis Robilliard, Fabien Teytaud

    Pages 191-201

19. Multi-objective AI Planning: Comparing Aggregation and Pareto Approaches

    • Mostepha R. Khouadjia, Marc Schoenauer, Vincent Vidal, Johann Dréo, Pierre Savéant

    Pages 202-213

20. Predicting Genetic Algorithm Performance on the Vehicle Routing Problem Using Information Theoretic Landscape Measures

    • Mario Ventresca, Beatrice Ombuki-Berman, Andrew Runka

    Pages 214-225

This book constitutes the refereed proceedings of the 13th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2013, held in Vienna, Austria, in April 2013, colocated with the Evo* 2013 events EuroGP, EvoBIO, EvoMUSART, and EvoApplications. The 23 revised full papers presented were carefully reviewed and selected from 50 submissions. The papers present the latest research and discuss current developments and applications in metaheuristics - a paradigm to effectively solve difficult combinatorial optimization problems appearing in various industrial, economic, and scientific domains. Prominent examples of metaheuristics are ant colony optimization, evolutionary algorithms, greedy randomized adaptive search procedures, iterated local search, simulated annealing, tabu search, and variable neighborhood search. Applications include scheduling, timetabling, network design, transportation and distribution, vehicle routing, the travelling salesman problem, packing and cutting, satisfiability, and general mixed integer programming.

• genetic algorithms
• local search
• neighborhood search
• particle swarm optimization
• vehicle routing
• algorithm analysis and problem complexity

• Department of Computer Science, University of Leipzig, Leipzig, Germany

  Martin Middendorf

• IKERBASQUE, Basque Foundation for Science, Department of Computer Science and Artificial Intelligence, University of the Basque Country, Donostia, Spain

  Christian Blum

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