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[null,null,"Titolo",null,"Artificial Evolution : 13th International Conference, Évolution Artificielle, EA 2017, Paris, France, October 25–27, 2017, Revised Selected Papers / / edited by Evelyne Lutton, Pierrick Legrand, Pierre Parrend, Nicolas Monmarché, Marc Schoenauer"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018"]
[null,null,"ISBN",null,"9783319781334"]
[{"$nulls":4},"3319781332"]
[null,null,"Edizione",null,"[1st ed. 2018.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XVI, 231 p. 77 illus.)"]
[null,null,"Collana",null,"Theoretical Computer Science and General Issues, , 2512-2029 ; ; 10764"]
[null,null,"Disciplina",null,"005.1"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Algorithms"]
[{"$nulls":4},"Computer science - Mathematics"]
[{"$nulls":4},"Discrete mathematics"]
[{"$nulls":4},"Numerical analysis"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Discrete Mathematics in Computer Science"]
[{"$nulls":4},"Numerical Analysis"]
[{"$nulls":4},"Mathematical Applications in Computer Science"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Évolution Artificielle 2017 - EA 2017 -- Abstracts of Invited Talks -- The Cartography of Computational Search Spaces -- Progressive Data Analysis: A New Computation Paradigm for Scalability in Exploratory Data Analysis -- Contents -- On the Design of a Master-Worker Adaptive Algorithm Selection Framework -- 1 Introduction -- 2 Related Works -- 2.1 Sequential Adaptive Algorithm Selection -- 2.2 Parallel Adaptive Algorithm Selection -- 2.3 Benchmarks: The Fitness Cloud Model -- 3 M/W Framework Description -- 3.1 Aggregation of Local Reward Values -- 3.2 Homogeneous vs. Heterogeneous Adaptive Selection -- 4 Experimental Analysis -- 4.1 Overall Relative Performance -- 4.2 Analysis of the Reward Aggregation Functions -- 4.3 Analysis of the Heterogeneity Scenarios -- 5 Conclusions -- References -- Comparison of Acceptance Criteria in Randomized Local "]
[{"$nulls":4},"Searches -- 1 Introduction -- 2 Literature Review -- 3 Experimental Setup -- 4 Experiments on the Quadratic Assignment Problem -- 5 Experiments on the Permutation Flow-Shop Problem -- 6 Conclusions -- References -- A Fitness Landscape View on the Tuning of an Asynchronous Master-Worker EA for Nuclear Reactor Design -- 1 Introduction -- 2 Preliminaries -- 2.1 Evolutionary Optimization for Nuclear Energy Problems -- 2.2 Parallel Evolutionary Algorithms -- 2.3 Landscape Aware Parameter Tuning -- 3 Problem Definition -- 3.1 Description of the System -- 3.2 Criterion of Interest -- 4 Asynchronous Parallel EA -- 4.1 Algorithm Definition -- 4.2 Mutation Operator -- 5 Experimental Analysis -- 5.1 Baseline Parameters Setting -- 5.2 Impact of the Mutation Parameters -- 5.3 Fitness Landscape Analysis -- 6 Conclusions -- References -- Sampled Walk and Binary Fitness Landscapes Exploration -- 1 Introduction -- 2 Fitness Landscapes -- 3 Partial Neighborhood Local Searches."]
[{"$nulls":4},"4 Analysis on Binary Fitness Landscapes -- 4.1 Experimental Protocol -- 4.2 Results -- 4.3 Landscapes Ruggedness and Partial Neighborhood LS Efficiency -- 5 Conclusion -- References -- Semantics-Based Crossover for Program Synthesis in Genetic Programming -- 1 Introduction -- 2 Related Work -- 2.1 Semantics -- 2.2 Semantic Crossover -- 3 Semantics in Program Synthesis -- 3.1 Semantic Similarity Measure with Traces -- 3.2 Semantic Crossover for Program Synthesis -- 4 Experimental Setup -- 4.1 Benchmark Problems -- 5 Results -- 5.1 Successful Runs and Fitness -- 5.2 Parent Comparison -- 5.3 Types Selected for Similarity Measurement -- 6 Conclusion and Future Work -- References -- On the Use of Dynamic GP Fitness Cases in Static and Dynamic Optimisation Problems -- 1 Introduction -- 2 Related Work -- 2.1 Fitness Cases in Genetic Programming -- 2.2 Promoting and Maintaining Diversity -- 3 Proposed Approaches -- 3.1 Dynamic Fitness Cases -- 3.2 Kendall Tau Distance -- 4 Experimental Setup -- 5 Results and Discussion -- 5.1 Performance on a Static Setting -- 5.2 Performance on a Dynamic Setting -- 5.3 Analysis of the Number of Created Individuals -- 5.4 Size of GP Programs -- 6 Conclusions -- References -- MEMSA: A Robust Parisian EA for Multidimensional Multiple Sequence Alignment -- 1 Introduction -- 1.1 Multiple Sequence Alignment (MSA) -- 1.2 Evolutionary Algorithms for MSA -- 1.3 Parisian Evolution Approach -- 2 Genetic Algorithm with Parisian Approach for MSA -- 2.1 Individuals/Patches -- 2.2 Initialisation -- 2.3 Crossover -- 2.4 Mutator -- 2.5 Evaluation -- 2.6 Diversity Preservation -- 2.7 Selection of Individuals for the New Generation -- 2.8 Patchwork to Create an MSA -- 2.9 Run Parameters and Behaviour of the Algorithm -- 3 Experiments and Validation -- 4 Discussion and Conclusion -- References."]
[{"$nulls":4},"Basic, Dual, Adaptive, and Directed Mutation Operators in the Fly Algorithm -- 1 Introduction -- 2 Problem Definition and Motivations -- 3 Overview of the Fly Algorithm for PET Reconstruction -- 4 Varying Mutation Operators in the Fly Algorithm -- 4.1 Basic Mutation -- 4.2 Adaptive Mutation Variance -- 4.3 Dual Mutation -- 4.4 Directed Mutation -- 5 Results -- 6 Conclusion -- References -- A New High-Level Relay Hybrid Metaheuristic for Black-Box Optimization Problems -- 1 Introduction -- 2 Presentation of the Hybridized Components -- 2.1 Overview of MLSDO Algorithm -- 2.2 Overview of SHADE Algorithm -- 2.3 Overview of SPSO2011 Algorithm -- 3 The Proposed Hybrid Algorithm -- 4 Experimental Protocol and Parameter Setting -- 4.1 The BBOB 2015 Benchmark -- 4.2 The Black Box Optimization Competition -- 4.3 Parameter Setting -- 5 Experimental Results and Discussion -- 5.1 Results for the BBOB 2015 Benchmark -- 5.2 Results at the Black "]
[{"$nulls":4},"Box Optimization Competition -- 6 Conclusion -- References -- Improved Hybrid Iterative Tabu Search for QAP Using Distance Cooperation -- 1 Introduction -- 2 Background -- 3 Distributed and Cooperative Algorithms -- 3.1 Distributed Hybrid Iterative Tabu Search -- 3.2 Distance Cooperation Hybrid Iterative Tabu Search -- 4 Experimental Results -- 4.1 Platform and Tests -- 4.2 Parameters -- 4.3 Experimentation -- 4.4 Literature Comparison -- 5 Conclusion and Perspectives -- References -- H-ACO: A Heterogeneous Ant Colony Optimisation Approach with Application to the Travelling Salesman Problem -- 1 Introduction -- 2 Ant Colony Optimization -- 3 Heterogeneous ACO -- 4 Methodology -- 4.1 Travelling Salesman Problem bib2 -- 5 Experimental Setup -- 6 Heterogeneous ACO Results -- 6.1 Exploring the Ranges of Alpha and Beta -- 6.2 Comparison with Base Algorithms -- 7 Discussion, Conclusion and Future Work -- References."]
[{"$nulls":4},"Evolutionary Learning of Fire Fighting Strategies -- 1 Introduction -- 2 Fire Enclosement in a Discrete Grid Setting -- 2.1 A Goal Oriented Evolution Model -- 2.2 Evolutionary Algorithm -- 2.3 Experimental Results -- 2.4 Fire Enclosement Conclusion -- 3 Protection of a Highway -- 3.1 Evolution Models -- 3.2 Evolutionary Algorithm -- 3.3 Experimental Results -- 3.4 Highway Protection Conclusion -- 4 Future Work on Theoretical Threshold Questions -- References -- Evolutionary Optimization of Tone Mapped Image Quality Index -- 1 Introduction -- 2 Related Work -- 3 Algorithm -- 3.1 Tone Mapping -- 3.2 Evolutionary Optimization -- 4 Experimental Results -- 5 Conclusion -- References -- LIDeOGraM: An Interactive Evolutionary Modelling Tool -- 1 Introduction -- 2 Background -- 2.1 Food Complex Systems -- 2.2 Symbolic Regression -- 2.3 Production and Stabilisation Process of Lactic Acid Bacteria -- 3 Proposed Approach -- 4 Experimental Results -- 4.1 The Dataset -- 4.2 Search with Eureqa -- 4.3 Optimisation of the Global Model -- 5 Discussion -- 6 Conclusions -- References -- Automatic Configuration of GCC Using Irace -- 1 Introduction -- 2 Automatic Algorithm Configuration -- 3 Configuration Scenarios -- 4 GCC Configuration Scenarios Analysis -- 5 Experimental Results -- 6 Conclusion and Future Work -- References -- Offline Learning for Selection Hyper-heuristics with Elman Networks -- 1 Introduction -- 2 Methodology -- 2.1 HyFlex and the Offline Learning Database -- 2.2 Final Log Returns and the BEST Sequences -- 2.3 Elman Networks -- 2.4 Training Sets -- 2.5 The BLIND Hyper-heuristic -- 3 Results -- 3.1 Network Training -- 3.2 Evaluating the Elman Network Sequences -- 4 Conclusions -- References -- Author Index."]
[null,null,null,"Sommario/riassunto","This book constitutes the thoroughly refereed post-conference proceedings of the 13th International Conference on Artificial Evolution, EA 2017, held in Paris, France, in October 2017. The 16 revised papers were carefully reviewed and selected from 33 submissions. The papers cover a wide range of topics in the field of artificial evolution, such as evolutionary computation, evolutionary optimization, co-evolution, artificial life, population dynamics, theory, algorithmics and modeling, implementations, application of evolutionary paradigms to the real world (industry, biosciences, ...), other biologically-inspired paradigms (swarm, artificial ants, artificial immune systems, cultural algorithms...), memetic algorithms, multi-objective optimisation, constraint handling, parallel algorithms,, dynamic optimization, machine learning and hybridization with other soft computing techniques."]