Evolutionary Computation in Combinatorial Optimization

Coverage in this proceedings volume presents the latest research and details current developments and applications in metaheuristics, a paradigm to effectively solve difficult combinatorial optimization problems in industry, economics, and science.

Metaheuristics have been shown to be e?ective for di?cult combinatorial - timization problems appearing in various industrial, economical, and scienti?c domains. Prominent examples of metaheuristics are evolutionary algorithms, tabu search, simulated annealing, scatter search, memetic algorithms, variable neighborhood search, iterated local search, greedy randomized adaptive search procedures, ant colony optimization and estimation of distribution algorithms. Problems solved successfully include scheduling, timetabling, network design, transportation and distribution, vehicle routing, the travelling salesman pr- lem, packing and cutting, satis?ability and general mixed integer programming. EvoCOPbeganin2001andhasbeenheldannuallysincethen.Itwasthe?rst event speci?cally dedicated to the application of evolutionary computation and related methods to combinatorial optimization problems. Originally held as a workshop,EvoCOPbecameaconferencein2004.Theeventsgaveresearchersan excellent opportunity to present their latest research and to discuss current - velopments and applications. Following the general trend of hybrid metaheur- tics and diminishing boundaries between the di?erent classes of metaheuristics, EvoCOP has broadened its scope over the last years and invited submissions on any kind of metaheuristic for combinatorial optimization.

Inhalt
Adaptive Tabu Tenure Computation in Local Search.- A Conflict Tabu Search Evolutionary Algorithm for Solving Constraint Satisfaction Problems.- Cooperative Particle Swarm Optimization for the Delay Constrained Least Cost Path Problem.- Effective Neighborhood Structures for the Generalized Traveling Salesman Problem.- Efficient Local Search Limitation Strategies for Vehicle Routing Problems.- Evolutionary Local Search for the Minimum Energy Broadcast Problem.- Exploring Multi-objective PSO and GRASP-PR for Rule Induction.- An Extended Beam-ACO Approach to the Time and Space Constrained Simple Assembly Line Balancing Problem.- Graph Colouring Heuristics Guided by Higher Order Graph Properties.- A Hybrid Column Generation Approach for the Berth Allocation Problem.- Hybrid Metaheuristic for the Prize Collecting Travelling Salesman Problem.- An ILS Based Heuristic for the Vehicle Routing Problem with Simultaneous Pickup and Delivery and Time Limit.- An Immune Genetic Algorithm Based on Bottleneck Jobs for the Job Shop Scheduling Problem.- Improved Construction Heuristics and Iterated Local Search for the Routing and Wavelength Assignment Problem.- Improving Metaheuristic Performance by Evolving a Variable Fitness Function.- Improving Query Expansion with Stemming Terms: A New Genetic Algorithm Approach.- Inc*: An Incremental Approach for Improving Local Search Heuristics.- Metaheuristics for the Bi-objective Ring Star Problem.- Multiobjective Prototype Optimization with Evolved Improvement Steps.- Optimising Multiple Kernels for SVM by Genetic Programming.- Optimization of Menu Layouts by Means of Genetic Algorithms.- A Path Relinking Approach with an Adaptive Mechanism to Control Parameters for the Vehicle Routing Problem with Time Windows.- Reactive Stochastic LocalSearch Algorithms for the Genomic Median Problem.- Solving Graph Coloring Problems Using Learning Automata.