Evolutionary Computation in Combinatorial Optimization : 17th European Conference, EvoCOP 2017, Amsterdam, The Netherlands, April 19-21, 2017, Proceedings /
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 pap...
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Corporate Authors: | |
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Group Author: | ; |
Published: |
Springer International Publishing : Imprint: Springer,
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Publisher Address: | Cham : |
Publication Dates: | 2017. |
Literature type: | eBook |
Language: | English |
Series: |
Lecture Notes in Computer Science,
10197 |
Subjects: | |
Online Access: |
http://dx.doi.org/10.1007/978-3-319-55453-2 |
Summary: |
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 al |
Carrier Form: | 1 online resource (XII, 249 pages): illustrations. |
ISBN: | 9783319554532 |
Index Number: | QA297 |
CLC: | O241 |
Contents: | 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 Co |