Hybrid Metaheuristics : 4th International Workshop, HM 2007, Dortmund, Germany, October 8-9, 2007. Proceedings / edited by Thomas Bartz-Beielstein, María José Blesa Aguilera, Christian Blum, Boris Naujoks, Andrea Roli, Günter Rudolph, Michael Sampels.

Springer eBooks

Evolutionary Local Search for the Super-Peer Selection Problem and the p-Hub Median Problem -- An Effective Memetic Algorithm with Population Management for the Split Delivery Vehicle Routing Problem -- Empirical Analysis of Two Different Metaheuristics for Real-World Vehicle Routing Problems -- Guiding ACO by Problem Relaxation: A Case Study on the Symmetric TSP -- Hybrid Local Search Techniques for the Resource-Constrained Project Scheduling Problem -- Evolutionary Clustering Search for Flowtime Minimization in Permutation Flow Shop -- A Hybrid ILS Heuristic to the Referee Assignment Problem with an Embedded MIP Strategy -- On the Combination of Constraint Programming and Stochastic Search: The Sudoku Case -- Improvement Strategies for the F-Race Algorithm: Sampling Design and Iterative Refinement -- Using Branch & Bound Concepts in Construction-Based Metaheuristics: Exploiting the Dual Problem Knowledge -- Gradient-Based/Evolutionary Relay Hybrid for Computing Pareto Front Approximations Maximizing the S-Metric -- A Hybrid VNS for Connected Facility Location -- A Memetic Algorithm for the Optimum Communication Spanning Tree Problem -- Hybrid Numerical Optimization for Combinatorial Network Problems.

This book constitutes the refereed proceedings of the 4th International Workshop on Hybrid Metaheuristics, HM 2007, held in Dortmund, Germany, in October 2007. The 14 revised full papers presented were carefully reviewed and selected from 37 submissions. The papers discuss specific aspects of hybridization of metaheuristics, hybrid metaheuristics design, development and testing. With increasing attention to methodological aspects, from both the empirical and theoretical sides, the papers show a representative sample of research in the field of hybrid metaheuristics. Some papers put special emphasis on the experimental analysis and statistical assessment of results, some are also an example of the integration of metaheuristics with mathematical programming, constraint satisfaction or machine learning techniques.

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