Evolutionary Computation in Combinatorial Optimization 1st edition by Martin Middendorf, Christian Blum ISBN 3642371981 9783642371981

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Product details:

ISBN 10:    3642371981
ISBN 13: 978-3642371981
Author:         Martin Middendorf, Christian Blum

Evolutionary Computation in Combinatorial Optimization 1st Table of contents:

1. A Hyper-heuristic with a Round Robin Neighbourhood Selection
2. A Multiobjective Approach Based on the Law of Gravity and Mass Interactions for Optimizing Networks
3. A Multi-objective Feature Selection Approach Based on Binary PSO and Rough Set Theory
4. A New Crossover for Solving Constraint Satisfaction Problems
5. A Population-Based Strategic Oscillation Algorithm for Linear Ordering Problem with Cumulative Costs
6. A Study of Adaptive Perturbation Strategy for Iterated Local Search
7. Adaptive MOEA/D for QoS-Based Web Service Composition
8. An Analysis of Local Search for the Bi-objective Bidimensional Knapsack Problem
9. An Artificial Immune System Based Approach for Solving the Nurse Re-rostering Problem
10. Automatic Algorithm Selection for the Quadratic Assignment Problem Using Fitness Landscape Analysis
11. Balancing Bicycle Sharing Systems: A Variable Neighborhood Search Approach
12. Combinatorial Neighborhood Topology Particle Swarm Optimization Algorithm for the Vehicle Routing Problem
13. Dynamic Evolutionary Membrane Algorithm in Dynamic Environments
14. From Sequential to Parallel Local Search for SAT
15. Generalizing Hyper-heuristics via Apprenticeship Learning
16. High-Order Sequence Entropies for Measuring Population Diversity in the Traveling Salesman Problem
17. Investigating Monte-Carlo Methods on the Weak Schur Problem
18. Multi-objective AI Planning: Comparing Aggregation and Pareto Approaches
19. Predicting Genetic Algorithm Performance on the Vehicle Routing Problem Using Information Theoretic Landscape Measures
20. Single Line Train Scheduling with ACO
21. Solving Clique Covering in Very Large Sparse Random Graphs by a Technique Based on k-Fixed Coloring Tabu Search
22. Solving the Virtual Network Mapping Problem with Construction Heuristics, Local Search and Variable Neighborhood Descent
23. The Generate-and-Solve Framework Revisited: Generating by Simulated Annealing

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