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E-grāmata: Evolutionary Computation in Combinatorial Optimization: 17th European Conference, EvoCOP 2017, Amsterdam, The Netherlands, April 19-21, 2017, Proceedings

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  • Formāts: PDF+DRM
  • Sērija : Lecture Notes in Computer Science 10197
  • Izdošanas datums: 03-Apr-2017
  • Izdevniecība: Springer International Publishing AG
  • Valoda: eng
  • ISBN-13: 9783319554532
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Evolutionary Computation in Combinatorial Optimization: 17th European Conference, EvoCOP 2017, Amsterdam, The Netherlands, April 19-21, 2017, ProceedingsPalielināt
  • Formāts: PDF+DRM
  • Sērija : Lecture Notes in Computer Science 10197
  • Izdošanas datums: 03-Apr-2017
  • Izdevniecība: Springer International Publishing AG
  • Valoda: eng
  • ISBN-13: 9783319554532
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This book constitutes the refereed proceedings of the 17th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2017, held in Amsterdam, The Netherlands, in April 2017, co-located with the Evo*2017 events EuroGP, EvoMUSART and EvoApplications.





The 16 revised full papers presented were carefully reviewed and selected from 39 submissions. The papers cover both empirical and theoretical studies on a wide range of academic and real-world applications. The methods include evolutionary and memetic algorithms, large neighborhood search, estimation of distribution algorithms, beam search, ant colony optimization, hyper-heuristics and matheuristics. Applications include both traditional domains, such as knapsack problem, vehicle routing, scheduling problems and SAT; and newer domains such as the traveling thief problem, location planning for car-sharing systems and spacecraft trajectory optimization. Papers also study important concepts such as pseudo-backbones, phase transitions in local optima networks, and the analysis of operators. This wide range of topics makes the EvoCOP proceedings an important source for current research trends in combinatorial optimization.
A Computational Study of Neighborhood Operators for Job-shop Scheduling
Problems with Regular Objectives.- A Genetic Algorithm for Multi-Component
Optimization Problems: the Case of the Travelling Thief Problem.- A Hybrid
Feature Selection Algorithm Based on Large Neighborhood Search.- A Memetic
Algorithm to Maximise the Employee Substitutability in Personnel Shift
Scheduling.- Construct, Merge, Solve and Adapt versus Large Neighborhood
Search for Solving the Multi-Dimensional Knapsack Problem: Which One Works
Better When.- Decomposing SAT Instances with Pseudo Backbones.- Efficient
Consideration of Soft Time Windows in a Large Neighborhood Search for the
Districting and Routing Problem for Security Control.- Estimation of
Distribution Algorithms for the Firefighter Problem.- LCS-Based Selective
Route Exchange Crossover for the Pickup and Delivery Problem with Time
Windows.- Multi-rendezvous Spacecraft Trajectory Optimization with Beam
P-ACO.- Optimizing Charging Station Locations for Electric Car-Sharing
Systems.- Selection of Auxiliary Objectives Using Landscape Features and
Offline Learned Classifier.- Sparse, Continuous Policy Representations for
Uniform Online Bin Packing via Regression of Interpolants.- The Weighted
Independent Domination Problem: ILP Model and Algorithmic 
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