Such approaches are able to provide optimal or quasi-optimal solutions to a plethora of difficult combinatorial optimisation problems.The application of metaheuristics to combinatorial optimisation is an active field in which new theoretical developments, new algorithmic models, and new application areas are continuously emerging.

Theory and Methodology.- An Evolutionary Algorithm for the Solution of Two-Variable Word Equations in Partially Commutative Groups.- Determining Whether a Problem Characteristic Affects Heuristic Performance.- Performance and Scalability of Genetic Algorithms on NK-Landscapes.- Engineering Stochastic Local Search Algorithms: A Case Study in Estimation-Based Local Search for the Probabilistic Travelling Salesman Problem.- Hybrid Approaches.- A Lagrangian Decomposition/Evolutionary Algorithm Hybrid for the Knapsack Constrained Maximum Spanning Tree Problem.- A Hybrid Optimization Framework for Cutting and Packing Problems.- A Hybrid Genetic Algorithm for the DNA Fragment Assembly Problem.- A Memetic-Neural Approach to Discover Resources in P2P Networks.- Constrained Problems.- An Iterative Heuristic Algorithm for Tree Decomposition.- Search Intensification in Metaheuristics for Solving the Automatic Frequency Problem in GSM.- Contraction-Based Heuristics to Improve the Efficiency of Algorithms Solving the Graph Colouring Problem.- Scheduling.- Different Codifications and Metaheuristic Algorithms for the Resource Renting Problem with Minimum and Maximum Time Lags.- A Simple Optimised Search Heuristic for the Job Shop Scheduling Problem.- Parallel Memetic Algorithms for Independent Job Scheduling in Computational Grids.- Routing and Travelling Salesman Problems.- Reducing the Size of Travelling Salesman Problem Instances by Fixing Edges.- Algorithms for Large Directed Capacitated Arc Routing Problem Instances.- An Evolutionary Algorithm with Distance Measure for the Split Delivery Capacitated Arc Routing Problem.- A Permutation Coding with Heuristics for the Uncapacitated Facility Location Problem.

Combinatorial optimisation is a ubiquitous discipline whose usefulness spans vast applications domains. The intrinsic complexity of most combinatorial optimisation problems makes classical methods unaffordable in many cases. To acquire practical solutions to these problems requires the use of metaheuristic approaches that trade completeness for pragmatic effectiveness. Such approaches are able to provide optimal or quasi-optimal solutions to a plethora of difficult combinatorial optimisation problems. The application of metaheuristics to combinatorial optimisation is an active field in which new theoretical developments, new algorithmic models, and new application areas are continuously emerging. This volume presents recent advances in the area of metaheuristiccombinatorial optimisation, with a special focus on evolutionary computation methods. Moreover, it addresses local search methods and hybrid approaches. In this sense, the book includes cutting-edge theoretical, methodological, algorithmic and applied developments in the field, from respected experts and with a sound perspective.

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