Details

Autor(en) / Beteiligte

• Sim, Kevin[Herausgeber]
• Kaufmann, Paul[Herausgeber]

Titel

Applications of Evolutionary Computation : 21st International Conference, EvoApplications 2018, Parma, Italy, April 4-6, 2018, Proceedings [electronic resource]

Auflage

1st ed. 2018

Ort / Verlag

Cham : Springer International Publishing

Erscheinungsjahr

2018

Link zum Volltext

• SpringerLink Books - AutoHoldings

Beschreibungen/Notizen

• Includes index.
• Intro -- Volume Editors -- Preface -- Organization -- Contents -- EvoBAFIN -- Multi-objective Cooperative Coevolutionary Algorithm with Dynamic Species-Size Strategy -- Abstract -- 1 Introduction -- 2 Multi-objective CCA with Dynamic Problem Decomposition -- 2.1 Dynamic Species-Size -- 2.2 Dynamic Process -- 2.3 Collaborator Selection Method -- 2.4 DMOCCA Main Algorithm -- 3 Formulation of CCMVPOP -- 4 Computational Experiments -- 4.1 Data -- 4.2 Parameter Setting -- 4.3 Computational Results -- 4.4 Effects of Implementing the Dynamic Species-Size Strategy -- 5 Conclusions -- References -- EvoBIO -- Task Classification Using Topological Graph Features for Functional M/EEG Brain Connectomics -- 1 Introduction -- 2 Model Selection as an Optimization Problem -- 3 Models and Methods -- 4 Experiments and Results -- 5 Conclusions and Future Research Lines -- References -- Feature Selection for Detecting Gene-Gene Interactions in Genome-Wide Association Studies -- 1 Introduction -- 2 Methods -- 2.1 Datasets -- 2.2 Quantification of Pairwise Interactions Using Information Gain -- 2.3 Feature Selection Algorithms -- 3 Results -- 3.1 Feature Selection Algorithms on the Simulated Data -- 3.2 Feature Selection Algorithms on the CRC Data -- 4 Discussion -- References -- Fitness Functions Evaluation for Segmentation of Lymphoma Histological Images Using Genetic Algorithm -- 1 Introduction -- 2 Materials and Methods -- 2.1 Images Dataset -- 2.2 Proposed Algorithm -- 2.3 Evaluation Metrics -- 3 Results and Discussion -- 4 Conclusion -- References -- Mutual Information Iterated Local Search: A Wrapper-Filter Hybrid for Feature Selection in Brain Computer Interfaces -- 1 Introduction -- 2 Background -- 2.1 Filters -- 2.2 Wrappers -- 2.3 Hybrid Approaches -- 3 Proposed Method -- 3.1 Iterated Local Search.
• 3.2 Minimal Redundancy Maximal Relevance-Iterated Local Search -- 4 Methodology -- 4.1 Datasets -- 4.2 Features -- 4.3 Solution Size -- 4.4 Classifiers -- 5 Results and Discussion -- 6 Conclusion -- References -- Automatic Segmentation of Neurons in 3D Samples of Human Brain Cortex -- Abstract -- 1 Introduction -- 2 Materials and Methods -- 2.1 Sample Collection and Preparation -- 2.2 Imaging: Two-Photon Fluorescence Microscopy -- 2.3 Image Stitching -- 2.4 Pattern-Level Segmentation by CNN -- 3 Results -- 4 Discussion and Conclusion -- Acknowledgements -- References -- Analysis of Relevance and Redundance on Topoisomerase 2b (TOP2B) Binding Sites: A Feature Selection Approach -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data -- 2.2 Classification -- 2.3 Feature Selection -- 3 Experimental Results -- 3.1 Baseline Classification Results -- 3.2 Feature Selection -- 4 Feature Analysis -- 4.1 Baseline Classification -- 4.2 Feature Selection -- 5 Conclusions and Future Works -- References -- EvoCOMNET -- Multimodal Transportation Network Design Using Physarum Polycephalum-Inspired Multi-agent Computation Methods -- 1 Introduction -- 2 Previous Work -- 3 Research Methodology -- 3.1 Model Background -- 3.2 The Multimodal Physarum Model -- 4 Model Evaluation -- 4.1 Basic Network Performance Analysis -- 4.2 Implementation in Real-World Context -- 5 Conclusions and Future Research -- References -- Improving Multi-objective Evolutionary Influence Maximization in Social Networks -- 1 Introduction -- 2 Background and Related Work -- 2.1 Models for Influence Propagation and Problem Formulation -- 2.2 Existing Solutions for Influence Maximization -- 3 Proposed Approach -- 4 Experimental Evaluation -- 4.1 Benchmarks -- 4.2 Experimental Results -- 5 Conclusions -- References -- Social Relevance Index for Studying Communities in a Facebook Group of Patients.
• 1 Introduction -- 2 Related Works -- 3 The Relevance Index Approach -- 4 HyReSS: Hybrid Relevant Set Search -- 4.1 Genetic Algorithm -- 4.2 Variable Relevance-Based Local Search -- 4.3 Variable Frequency-Based Search -- 4.4 CRS Cardinality-Based Search -- 4.5 Merging -- 5 Experimental Results -- 5.1 Dataset Description -- 5.2 HyReSS Performances -- 5.3 Social Network Results -- 6 Conclusion -- References -- A Fast Metaheuristic for the Design of DVB-T2 Networks -- 1 Introduction -- 2 An Optimization Model for DVB-T2 Network Design -- 2.1 Strengthening the Formulation DVB-MILP -- 3 A Metaheuristic for DVB-T2 Network Design -- 3.1 Feasible Solution Construction -- 3.2 MILP Improvement Heuristic -- 3.3 The Complete Algorithm -- 4 Computational Tests -- 5 Conclusion and Future Work -- References -- EvoCOMPLEX -- A Genetic Algorithm for Community Detection in Attributed Graphs -- 1 Introduction -- 2 Problem Definition -- 3 @NetGA Description -- 4 Experimental Evaluation -- 4.1 Datasets -- 4.2 Algorithms in Comparison -- 4.3 Evaluation Measures -- 4.4 Results -- 5 Conclusion -- References -- Maximizing the Effect of Local Disturbance in the Dynamics of Opinion Formation -- Abstract -- 1 Introduction -- 2 Model -- 3 Genetic Algorithm -- 4 Result -- 5 Conclusion -- Acknowledgement -- References -- Accelerating the Computation of Solutions in Resource Allocation Problems Using an Evolutionary Approach and Multiagent Reinforcement Learning -- 1 Introduction -- 2 Multiagent Reinforcement Learning -- 3 Related Work -- 4 Methods: General Scheme -- 5 Methods: Specific Problem -- 5.1 Instantiation to a Congestion Game -- 5.2 Traffic Networks -- 6 Results -- 6.1 Network: OW -- 6.2 Network: SF -- 6.3 Network: Braess Paradox -- 6.4 Discussion -- 7 Conclusions and Future Work -- References -- EvoENERGY.
• Achieving Optimized Decisions on Battery Operating Strategies in Smart Buildings -- 1 Introduction -- 2 Related Work -- 3 Scenario and Setup: Smart Residential Building -- 3.1 Building Model and Battery Energy Storage System Model -- 3.2 Building Energy Management System -- 4 Battery System Controller: Approach and Optimization -- 4.1 Non-optimized and Optimized Operating Strategies -- 4.2 Integration into the Optimization -- 4.3 Handling of Uncertainty in Predictions -- 5 Results and Discussion -- 5.1 Exemplary Optimized Day -- 5.2 Discussion of the Results -- 6 Conclusion and Outlook -- References -- Phase-Space Sampling of Energy Ensembles with CMA-ES -- 1 Introduction -- 2 Scheduling and Flexibility Modeling -- 3 Phase Space Sampling -- 4 CMA-ES for Optimized Sampling -- 5 Results -- 6 Conclusion -- References -- Many-Objective Optimization of Mission and Hybrid Electric Power System of an Unmanned Aircraft -- Abstract -- 1 Introduction -- 1.1 Evolutionary Methods -- 2 The Optimization Problem -- 2.1 Inputs of the Optimization -- 2.2 Optimization Methods and Goals -- 3 Performance Analysis -- 3.1 Results of the Simplified Problem -- 3.2 Results of the Complete Problem (Many-Objective Optimization) -- 4 Discussion of the Results -- 5 Conclusions -- References -- Evolving Controllers for Electric Vehicle Charging -- 1 Introduction -- 2 Evolution of Controllers -- 3 Experiments -- 4 Conclusions and Future Work -- References -- Network Coordinated Evolution: Modeling and Control of Distributed Systems Through On-line Genetic PID-Control Optimization Search -- 1 Introduction -- 2 Problem Description -- 3 Network Coordinated Evolution -- 3.1 On-line Genetic Search Algorithm -- 3.2 Graph Database -- 4 Implementation -- 4.1 Genetic Search Implementation -- 4.2 Testbed Realization -- 5 Results -- 6 Conclusion -- References -- EvoGAMES.
• Piecemeal Evolution of a First Person Shooter Level -- 1 Introduction -- 2 Background Work on Map Sketches -- 3 Methodology -- 3.1 Evolving the Ground Floor -- 3.2 Creating the Top Floor from the Ground Floor -- 3.3 Evolving both Floors -- 3.4 Post-processing to Create the Final Room -- 4 Experiments -- 4.1 Comparing Level Structures -- 4.2 Comparing Level Patterns -- 5 Discussion -- 6 Conclusion -- References -- Online-Trained Fitness Approximators for Real-World Game Balancing -- 1 Introduction -- 1.1 Motivation -- 1.2 Previous Work -- 1.3 Structure -- 2 Methodology -- 2.1 Ms Pacman -- 2.2 TORCS -- 2.3 Genetic Algorithm -- 2.4 Approximator Integration -- 2.5 Neural Network -- 2.6 C4.5 Decision Trees -- 2.7 K-Nearest Neighbours -- 2.8 Experiments -- 3 Results -- 3.1 PacMan -- 3.2 TORCS -- 4 Discussion and Conclusion -- References -- Recomposing the Pokémon Color Palette -- 1 Introduction -- 2 Related Work -- 3 Processing the Pokémon Dataset -- 3.1 The Dataset -- 3.2 Decomposing Pokémon Sprites -- 3.3 Analysis of Pokémon Sprite Metrics -- 4 Building a Classifier for Pokémon Types -- 5 Evolving the Pokémon Pallette -- 5.1 Customizing a Single Pokémon -- 5.2 Removing a Pokémon Type -- 5.3 Balancing the Number of Pokémon Per Type -- 6 Discussion -- 7 Conclusion -- References -- Mapping Chess Aesthetics onto Procedurally Generated Chess-Like Games -- 1 Introduction -- 2 Background Work -- 2.1 Procedural Content Generation -- 2.2 Simplified Boardgames -- 3 Methodology -- 3.1 Strategic Metrics -- 3.2 Visual Metrics -- 3.3 Mapping from General Games to Chess -- 3.4 Representation -- 3.5 Evolution and Its Variants -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Evolving a TORCS Modular Fuzzy Driver Using Genetic Algorithms -- 1 Introduction -- 2 State of the Art -- 3 Experimental Setup -- 3.1 The TORCS Simulator -- 3.2 Fuzzy Controller.
• 4 Optimizing the Fuzzy Controllers with GA.
• This book constitutes the refereed conference proceedings of the 21st International Conference on the Applications of Evolutionary Computation, EvoApplications 2018, held in Parma, Italy, in April 2018, collocated with the Evo* 2018 events EuroGP, EvoCOP, and EvoMUSART. The 59 revised full papers presented were carefully reviewed and selected from 84 submissions. EvoApplications 2018 combined research from 14 different domains: business analytics and finance (EvoBAFIN); computational biology (EvoBIO); communication networks and other parallel and distributed systems (EvoCOMNET); complex systems (EvoCOMPLEX); energy-related optimization (EvoENERGY); games and multi-agent systems (EvoGAMES); image analysis, signal processing and pattern recognition (EvoIASP); realworld industrial and commercial environments (EvoINDUSTRY); knowledge incorporation in evolutionary computation (EvoKNOW); continuous parameter optimization (EvoNUM); parallel architectures and distributed infrastructures (EvoPAR); evolutionary robotics (EvoROBOT); nature-inspired algorithms in software engineering and testing (EvoSET); and stochastic and dynamic environments (EvoSTOC).