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201226s2020 sz o 101 0 eng d |
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|a 1227240794
|a 1227389366
|a 1237465978
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|a 9783030589301
|q (electronic bk.)
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|a 10.1007/978-3-030-58930-1
|2 doi
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|a (OCoLC)1228033987
|z (OCoLC)1227240794
|z (OCoLC)1227389366
|z (OCoLC)1237465978
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|a QA76.9.A43
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|a HCDD
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| 245 |
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|a Heuristics for optimization and learning /
|c Farouk Yalaoui, Lionel Amodeo, El-Ghazali Talbi, editors.
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| 260 |
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|a Cham :
|b Springer,
|c 2020.
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| 300 |
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|a 1 online resource (444 pages)
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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|a Studies in computational intelligence ;
|v v. 906
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| 505 |
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|a Intro -- Preface -- Contents -- 1 Process Plan Generation for Reconfigurable Manufacturing Systems: Exact Versus Evolutionary-Based Multi-objective Approaches -- 1.1 Introduction -- 1.2 Literature Review -- 1.3 Problem Description and Mathematical Formulation -- 1.3.1 Problem Description -- 1.3.2 Mathematical Formulation -- 1.4 Proposed Approaches -- 1.4.1 Iterative Multi-Objective Integer Linear Program (I-MOILP) -- 1.4.2 Adapted Archived Multi-Objective Simulated-Annealing (AMOSA) -- 1.4.3 Adapted Non Dominated Sorting Genetic Algorithm II (NSGA-II) -- 1.5 Experimental Results and Analyses
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| 505 |
8 |
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|a 1.5.1 Experimental Scheme 1 -- 1.5.2 Experimental Scheme 2 -- 1.6 Conclusion -- References -- 2 On VNS-GRASP and Iterated Greedy Metaheuristics for Solving Hybrid Flow Shop Scheduling Problem with Uniform Parallel Machines and Sequence Independent Setup Time -- 2.1 Introduction -- 2.2 Description of the Hybrid Flow Shop Problem -- 2.3 Resolution -- 2.3.1 Initialization Heuristics -- 2.3.2 Metaheuristics -- 2.4 Numerical Simulation -- 2.4.1 Simulation Instances -- 2.4.2 Experimental Results -- 2.5 Conclusion -- References
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|a 3 A Variable Block Insertion Heuristic for the Energy-Efficient Permutation Flowshop Scheduling with Makespan Criterion -- 3.1 Introduction -- 3.2 Problem Formulation -- 3.3 Energy-Efficient VBIH Algorithm -- 3.3.1 Initial Population -- 3.3.2 Energy-Efficient Block Insertion Procedure -- 3.3.3 Energy-Efficient Insertion Local Search -- 3.3.4 Energy-Efficient Uniform Crossover and Mutation -- 3.3.5 Archive Set -- 3.4 Computational Results -- 3.5 Conclusions -- References -- 4 Solving 0-1 Bi-Objective Multi-dimensional Knapsack Problems Using Binary Genetic Algorithm -- 4.1 Introduction
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| 505 |
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|a 4.2 Literature Review -- 4.3 Problem Formulation -- 4.4 Bi-Objective BGA -- 4.5 Computational Results -- 4.6 Conclusion -- References -- 5 An Asynchronous Parallel Evolutionary Algorithm for Solving Large Instances of the Multi-objective QAP -- 5.1 Introduction -- 5.2 Related Works -- 5.3 The APM-MOEA Model -- 5.3.1 Global Search View of the Organizer -- 5.3.2 Asynchronous Communications -- 5.3.3 Control Islands -- 5.3.4 Local Search -- 5.4 Experimental Results -- 5.4.1 Performance Metrics -- 5.4.2 The GISMOO Algorithm -- 5.4.3 MQAP Instances -- 5.4.4 Experimental Conditions
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|a 5.4.5 Resolution of Small MQAP Instances -- 5.4.6 Resolution of Large MQAP Instances -- 5.5 Conclusion -- References -- 6 Learning from Prior Designs for Facility Layout Optimization -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Facility Layout Model -- 6.4 Similarity Model -- 6.4.1 Probabilistic Layout Model -- 6.4.2 Estimation -- 6.5 Similarity in Layout Optimization -- 6.6 Experiments -- 6.7 Discussion -- References -- 7 Single-Objective Real-Parameter Optimization: Enhanced LSHADE-SPACMA Algorithm -- 7.1 Enhanced LSHADE with Semi-parameter Adaptation Hybrid with CMA-ES (ELSHADE-SPACMA)
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|a 7.1.1 LSHADE Algorithm.
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|a This book is a new contribution aiming to give some last research findings in the field of optimization and computing. This work is in the same field target than our two previous books published: Recent Developments in Metaheuristics and Metaheuristics for Production Systems, books in Springer Series in Operations Research/Computer Science Interfaces. The challenge with this work is to gather the main contribution in three fields, optimization technique for production decision, general development for optimization and computing method and wider spread applications. The number of researches dealing with decision maker tool and optimization method grows very quickly these last years and in a large number of fields. We may be able to read nice and worthy works from research developed in chemical, mechanical, computing, automotive and many other fields.
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| 500 |
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|a Includes index.
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| 588 |
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|a Online resource; title from PDF title page (SpringerLink, viewed February 18, 2021).
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| 650 |
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|a Metaheuristics
|v Congresses.
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| 650 |
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0 |
|a CAD/CAM systems
|v Congresses.
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| 650 |
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7 |
|a CAD/CAM systems
|2 fast
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| 650 |
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7 |
|a Metaheuristics
|2 fast
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| 655 |
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|a proceedings (reports)
|2 aat
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| 655 |
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|a Conference papers and proceedings
|2 fast
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| 655 |
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|a Conference papers and proceedings.
|2 lcgft
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|a Actes de congrès.
|2 rvmgf
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| 700 |
1 |
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|a Yalaoui, Farouk.
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| 700 |
1 |
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|a Amodeo, Lionel.
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| 700 |
1 |
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|a Talbi, El-Ghazali,
|d 1965-
|1 https://id.oclc.org/worldcat/entity/E39PCjxPxHXw9RyP9hr9VXVYdP
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| 711 |
2 |
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|a International Conference on Metaheuristics and Nature Inspired Computing
|n (7th :
|d 2018 :
|c Marrakesh, Morocco)
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| 758 |
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|i has work:
|a Heuristics for optimization and learning (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCGbq8y4jXVdk6mXRjdykym
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
| 776 |
0 |
8 |
|i Print version:
|a Yalaoui, Farouk.
|t Heuristics for Optimization and Learning.
|d Cham : Springer International Publishing AG, ©2020
|z 9783030589295
|
| 830 |
|
0 |
|a Studies in computational intelligence ;
|v v. 906.
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-030-58930-1
|y Click for online access
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| 903 |
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|a SPRING-ROBOTICS2021
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| 994 |
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|a 92
|b HCD
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