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Evolutionary Computation in Combinatorial Optimization : 13th European Conference, EvoCOP 2013, Vienna, Austria, April 3-5, 2013. Proceedings
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 paper...
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| Auteurs principaux : | , , |
|---|---|
| Collectivité auteur : | |
| Format : | Livre |
| Langue : | anglais |
| Titre complet : | Evolutionary Computation in Combinatorial Optimization : 13th European Conference, EvoCOP 2013, Vienna, Austria, April 3-5, 2013. Proceedings / edited by Martin Middendorf, Christian Blum. |
| Publié : |
Berlin, Heidelberg :
Springer Berlin Heidelberg
, [20..] Cham : Springer Nature |
| Collection : | Theoretical computer science and general issues (Online) ; 7832 |
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| Documents associés : | Autre format:
Evolutionary Computation in Combinatorial Optimization Autre format: Evolutionary Computation in Combinatorial Optimization |
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| 200 | 1 | |a Evolutionary Computation in Combinatorial Optimization |e 13th European Conference, EvoCOP 2013, Vienna, Austria, April 3-5, 2013. Proceedings |f edited by Martin Middendorf, Christian Blum. | |
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| 330 | |a 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. | ||
| 359 | 1 | |a A Hyper-heuristic with a Round Robin Neighbourhood -- A Multiobjective Approach Based on the Law of Gravity and Mass Interactions for Optimizing Networks -- A Multi-objective Feature Selection Approach Based on Binary PSO and Rough Set Theory -- A New Crossover for Solving Constraint Satisfaction Problems -- A Population-Based Strategic Oscillation Algorithm for Linear Ordering Problem with Cumulative Costs -- A Study of Adaptive Perturbation Strategy for Iterated Local Search -- Adaptive MOEA/D for QoS-Based Web Service Composition -- An Analysis of Local Search for the Bi-objective Bidimensional Knapsack Problem -- An Artificial Immune System Based Approach for Solving the Nurse Re-rostering Problem -- Automatic Algorithm Selection for the Quadratic Assignment Problem Using Fitness Landscape Analysis -- Balancing Bicycle Sharing Systems: A Variable Neighborhood Search Approach -- Combinatorial Neighborhood Topology Particle Swarm Optimization Algorithm for the Vehicle Routing Problem -- Dynamic Evolutionary Membrane Algorithm in Dynamic Environments -- From Sequential to Parallel Local Search for SAT -- Generalizing Hyper-heuristics via Apprenticeship Learning -- High-Order Sequence Entropies for Measuring Population Diversity in the Traveling Salesman Problem -- Investigating Monte-Carlo Methods on the Weak Schur Problem -- Multi-objective AI Planning: Comparing Aggregation and Pareto Approaches -- Predicting Genetic Algorithm Performance on the Vehicle Routing Problem Using Information Theoretic Landscape -- Single Line Train Scheduling with ACO -- Solving Clique Covering in Very Large Sparse Random Graphs by a Technique Based on k-Fixed Coloring Tabu Search -- Solving the Virtual Network Mapping Problem with Construction Heuristics, Local Search and Variable Neighborhood Descent -- The Generate-and-Solve Framework Revisited: Generating by Simulated Annealing. | |
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