LEADER 02601nam 2200637 a 450 001 9910463130103321 005 20200520144314.0 010 $a0-7083-2589-0 035 $a(CKB)2670000000354811 035 $a(EBL)1173646 035 $a(OCoLC)846951412$z(OCoLC)843198810 035 $a(SSID)ssj0000909250 035 $a(PQKBManifestationID)11485943 035 $a(PQKBTitleCode)TC0000909250 035 $a(PQKBWorkID)10921811 035 $a(PQKB)11567158 035 $a(MiAaPQ)EBC1173646 035 $a(MiAaPQ)EBC1889152 035 $a(Au-PeEL)EBL1173646 035 $a(CaPaEBR)ebr10690532 035 $a(CaONFJC)MIL580761 035 $a(OCoLC)846951412 035 $a(EXLCZ)992670000000354811 100 $a20130504d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aWomen's writing in twenty-first-century France$b[electronic resource] $elife as literature /$fedited by Amaleena Damle? and Gill Rye 210 $aCardiff $cUniversity of Wales Press$d2013 215 $a1 online resource (316 p.) 225 1 $aFrench and francophone studies 300 $aDescription based upon print version of record. 311 $a0-7083-2588-2 320 $aIncludes bibliographical references and index. 327 $apt. 1. Women's writing in twenty-first-century France : trends and issues -- pt. 2. Society, culture, family -- pt. 3. Body, life, text -- pt. 4. Experiments, interfaces, aesthetics. 330 $aWomen's Writing in Twenty-First Century France is the first book-length publication on women-authored literature of this period, and comprises a collection of challenging critical essays that engage with the themes, trends and issues, and with the writers and their texts, of the first decade of the twenty-first century. 410 0$aFrench and francophone studies. 606 $aFrench literature$xWomen authors$xHistory and criticism 606 $aWomen authors, French$xHistory$y21st century 606 $aWomen and literature$zFrance$xHistory$y21st century 608 $aElectronic books. 615 0$aFrench literature$xWomen authors$xHistory and criticism. 615 0$aWomen authors, French$xHistory 615 0$aWomen and literature$xHistory 676 $a840.99287 701 $aDamle?$b Amaleena$0908312 701 $aRye$b Gill$0800737 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910463130103321 996 $aWomen's writing in twenty-first-century France$92031483 997 $aUNINA LEADER 09706nam 22007815 450 001 996465748203316 005 20230222060952.0 010 $a3-319-77449-2 024 7 $a10.1007/978-3-319-77449-7 035 $a(CKB)4100000002892501 035 $a(DE-He213)978-3-319-77449-7 035 $a(MiAaPQ)EBC6285861 035 $a(MiAaPQ)EBC5596424 035 $a(Au-PeEL)EBL5596424 035 $a(OCoLC)1029604862 035 $a(PPN)225550741 035 $a(EXLCZ)994100000002892501 100 $a20180302d2018 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEvolutionary Computation in Combinatorial Optimization$b[electronic resource] $e18th European Conference, EvoCOP 2018, Parma, Italy, April 4?6, 2018, Proceedings /$fedited by Arnaud Liefooghe, Manuel López-Ibáñez 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (XIV, 189 p. 31 illus.) 225 1 $aTheoretical Computer Science and General Issues,$x2512-2029 ;$v10782 311 $a3-319-77448-4 320 $aIncludes bibliographical references and index. 327 $aIntro -- Preface -- Organization -- Contents -- Better Runtime Guarantees via Stochastic Domination -- 1 Introduction -- 2 Stochastic Domination -- 3 Domination-Based Fitness Level Method -- 4 Beyond the Fitness Level Theorem -- 5 Structural Domination -- 6 Conclusion -- References -- On the Fractal Nature of Local Optima Networks -- 1 Introduction -- 2 Background -- 2.1 The Study of Fitness Landscapes -- 2.2 The Local Optima Network -- 2.3 The Fractal Dimension -- 2.4 Fractals and Fitness Landscapes -- 2.5 Fractals and Complex Networks -- 3 Experimental Setting -- 3.1 Test Problem -- 3.2 Metaheuristics -- 3.3 Fractal Analysis -- 4 Results -- 4.1 Fractals and Epistasis -- 4.2 Fractal Dimension and Search Performance -- 5 Discussion -- 5.1 The Fractal Shape of Local Optima Networks -- 5.2 Connections with Search Difficulty -- 6 Conclusions and Future Work -- References -- How Perturbation Strength Shapes the Global Structure of TSP Fitness Landscapes -- 1 Introduction -- 2 Definitions and Algorithms -- 3 Empirical Methodology -- 3.1 Instances -- 3.2 Sampling Method -- 3.3 Performance and Network Metrics -- 4 Results and Analysis -- 4.1 Visualisation -- 4.2 Performance and Network Metrics Results -- 4.3 Impact of Perturbation Strength on Success Rate -- 4.4 Correlation Analysis -- 4.5 Correlation Variance Between Instance Classes -- 5 Conclusions -- References -- Worst Improvement Based Iterated Local Search -- 1 Introduction -- 2 Definitions -- 2.1 Fitness Landscapes and Related Concepts -- 2.2 Bit-String Landscapes Instances -- 3 Worst Improvement Hill-Climbing -- 3.1 Pivoting Rules -- 3.2 Additional Experiments -- 4 Experimental Analysis -- 4.1 Experimental Protocol -- 4.2 Results -- 4.3 ILS Performance and Landscape Features -- 5 Conclusion -- References. 327 $aAutomatic Grammar-Based Design of Heuristic Algorithms for Unconstrained Binary Quadratic Programming -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 Grammar and the Heuristic Search Space -- 3.2 Automatic Design Using irace -- 4 Experiments and Results -- 4.1 Tuning with a Single Instance Set -- 4.2 Tuning with a Random Instance Set -- 5 Conclusions -- References -- Automatic Algorithm Configuration for the Permutation Flow Shop Scheduling Problem Minimizing Total Completion Time -- 1 Introduction -- 2 Automatic Algorithm Configuration -- 2.1 Grammar and Components -- 2.2 Solution Representation -- 2.3 Search Strategy -- 3 Computational Experiments -- 3.1 Benchmarks -- 3.2 Experimental Setup -- 3.3 Results -- 4 Conclusions -- References -- Data Clustering Using Grouping Hyper-heuristics -- 1 Introduction -- 2 Related Work -- 2.1 Solution Representation in Grouping Problems -- 2.2 Data Clustering -- 3 A Grouping Hyper-heuristic Approach to Solve Grouping Problems -- 3.1 Low Level Heuristics -- 3.2 Selection Hyper-heuristic Components -- 4 Application of Grouping Hyper-heuristics to Data Clustering -- 4.1 Experimental Data -- 4.2 Trials and Parameters Settings and CPU Specifications -- 4.3 Evaluation Criteria -- 4.4 Experimental Results and Remarks -- 5 Conclusion -- References -- Reference Point Adaption Method -1for Genetic Programming Hyper-Heuristic in Many-Objective Job Shop Scheduling -- 1 Introduction -- 2 Background -- 2.1 Problem Description of JSS -- 2.2 Related Work -- 3 Adaptive Reference Points for Many-Objective JSS -- 3.1 Fitness Evaluation -- 3.2 Reference Point Generation -- 4 Experiment Design -- 4.1 Parameter Settings -- 4.2 Data Set -- 4.3 Performance Measures -- 5 Results and Discussions -- 5.1 Overall Result -- 6 Conclusion -- References. 327 $aMOEA/DEP: An Algebraic Decomposition-Based Evolutionary Algorithm for the Multiobjective Permutation Flowshop Scheduling Problem -- 1 Introduction and Related Work -- 2 Multiobjective Optimization and MOEA/D Framework -- 3 Algebraic Differential Evolution for Permutations -- 4 MOEA/DEP -- 4.1 Initialization -- 4.2 Offsprings Generation -- 4.3 Population Update -- 4.4 Crossover for Permutations -- 5 Experiments -- 5.1 Parameters Calibration -- 5.2 Comparison with MEDA/D-MK -- 6 Conclusion and Future Work -- References -- An Evolutionary Algorithm with Practitioner's-Knowledge-Based Operators for the Inventory Routing Problem -- 1 Introduction -- 2 Problem Definition -- 3 Evolutionary Approach -- 3.1 Search Space and Solution Encoding -- 3.2 Initial Population -- 3.3 Recombination Operator -- 3.4 Date-Changing Mutation (DM) -- 3.5 Order-Changing Mutation (OM) -- 4 Experiments -- 5 Conclusions -- References -- A Multistart Alternating Tabu Search for Commercial Districting -- 1 Introduction -- 2 Definitions -- 3 Proposed Methods -- 3.1 Solution Construction -- 3.2 Optimizing Balance -- 3.3 Optimizing Compactness -- 3.4 Data Structures for Efficient Operations -- 4 Computational Experiments -- 4.1 Test Instances -- 4.2 Experimental Setup -- 4.3 Experiment 1: Constructive Algorithm -- 4.4 Experiment 2: Search Strategies -- 4.5 Experiment 3: Comparison with Existing Methods -- 5 Concluding Remarks -- References -- An Ant Colony Approach for the Winner Determination Problem -- 1 Introduction -- 1.1 Main Contributions -- 2 Winner Determination Problem -- 3 Literature Review -- 3.1 Exact Methods -- 3.2 Inexact Methods -- 4 Proposed Approach -- 4.1 Preprocessing Phase -- 4.2 Theoretical Convergence to Optimal for WDP -- 5 Randomized Pheromone Updating -- 5.1 Min-Max Pheromone Level -- 6 Randomized Graph Pruning -- 7 Experimental Results. 327 $a8 Conclusion and Future Research -- References -- Erratum to: On the Fractal Nature of Local Optima Networks -- Erratum to: Chapter "On the Fractal Nature of Local Optima Networks" in: A. Liefooghe and M. Lo?pez-Iba?n?ez (Eds.): Evolutionary Computation in Combinatorial Optimization, LNCS 10782, https://doi.org/10.1007/978-3-319-77449-7_2 -- Author Index. 330 $aThis book constitutes the refereed proceedings of the 18th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2018, held in Parma, Italy, in April 2018, co-located with the Evo* 2018 events EuroGP, EvoMUSART and EvoApplications. The 12 revised full papers presented were carefully reviewed and selected from 37 submissions. The papers cover a wide spectrum of topics, ranging from the foundations of evolutionary computation algorithms and other search heuristics, to their accurate design and application to both single- and multi-objective combinatorial optimization problems. Fundamental and methodological aspects deal with runtime analysis, the structural properties of fitness landscapes, the study of metaheuristics core components, the clever design of their search principles, and their careful selection and configuration by means of automatic algorithm configuration and hyper-heuristics. Applications cover conventional academic domains such as NK landscapes, binary quadratic programming, traveling salesman, vehicle routing, or scheduling problems, and also include real-world domains in clustering, commercial districting and winner determination. 410 0$aTheoretical Computer Science and General Issues,$x2512-2029 ;$v10782 606 $aNumerical analysis 606 $aAlgorithms 606 $aArtificial intelligence 606 $aComputer science?Mathematics 606 $aDiscrete mathematics 606 $aArtificial intelligence?Data processing 606 $aNumerical Analysis 606 $aAlgorithms 606 $aArtificial Intelligence 606 $aDiscrete Mathematics in Computer Science 606 $aData Science 615 0$aNumerical analysis. 615 0$aAlgorithms. 615 0$aArtificial intelligence. 615 0$aComputer science?Mathematics. 615 0$aDiscrete mathematics. 615 0$aArtificial intelligence?Data processing. 615 14$aNumerical Analysis. 615 24$aAlgorithms. 615 24$aArtificial Intelligence. 615 24$aDiscrete Mathematics in Computer Science. 615 24$aData Science. 676 $a005.432 702 $aLiefooghe$b Arnaud$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLópez-Ibáñez$b Manuel$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996465748203316 996 $aEvolutionary Computation in Combinatorial Optimization$92824693 997 $aUNISA