Fuzzy logic hybrid extensions of neural and optimization algorithms : theory and applications / / editors, Oscar Castillo, Patricia Melin

• Record Nr.: UNINA9910483557103321
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer, , [2021]; ©2021
• ISBN: 3-030-68776-7
• Descrizione fisica: 1 online resource (ix, 383 pages) : illustrations
• Collana: Studies in computational intelligence ; ; Volume 940
• Disciplina: 511.3
• Soggetti: Fuzzy logic; Neural networks (Computer science); Soft computing; Lògica difusa; Xarxes neuronals (Informàtica); Optimització matemàtica; Informàtica tova; Algorismes; Llibres electrònics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Intro -- Preface -- Contents -- Estimation of the Number of Filters in the Convolution Layers of a Convolutional Neural Network Using a Fuzzy Logic System -- 1 Introduction -- 2 Literature Review -- 2.1 Convolutional Neural Networks -- 2.2 GSA -- 2.3 FGSA -- 3 Proposed Method -- 4 Results and Discussion -- 5 Conclusions -- References -- Optimization of Membership Function Parameters for Fuzzy Controllers in Cruise Control Problem Using the Multi-verse Optimizer -- 1 Introduction -- 2 Fuzzy Systems -- 2.1 Mamdani Model -- 2.2 Sugeno Model -- 3 Control Systems -- 4 Metaheuristics and Multi-verse Optimizer -- 4.1 Multi-verse Optimizer -- 4.2 Applications of MVO -- 5 Test and Results -- 5.1 Benchmark Function Test and Results -- 5.2 Applications Test and Results -- 6 Conclusions -- References -- Performance Analysis of a Distributed Steady-State Genetic Algorithm ; Using Low-Power Computers -- 1 Introduction -- 2 Distributed Steady-State Genetic Algorithm -- 2.1 Application of Distributed Steady-State Genetic Algorithm in the n-Queens Problem -- 2.2 Application of Distributed Steady-State Genetic Algorithm in the Travelling Salesman Problem -- 3 Master-Slave Low Power Architecture -- 3.1 Rationale on Master-Slave Architecture Starting Procedure -- 3.2 Function Evaluation Task on Slave-Devices -- 3.3 Fail-Safe Algorithm on Master-Device -- 4 Computational Results -- 4.1 Experimental Setup -- 4.2 n-Queens Problem Experimental Arrangement Results -- 4.3 Travelling Salesman Problem Results -- 5 Conclusions and Future Work -- References -- Ensemble Recurrent Neural Networks for Complex Time Series Prediction with Integration Methods -- 1 Introduction -- 2 Problem Statement and Proposed Method -- 2.1 Analyze the Time Series -- 2.2 Creation of the Recurrent Neural Network -- 2.3 Integration by Average -- 2.4 Integration by Weighted Average.; 2.5 Integration by Gating Network -- 2.6 Type-1 and Type-2 Fuzzy System Integration -- 2.7 Generalized Type-2 Fuzzy System -- 3 Simulation Results -- 4 Conclusions -- References -- Genetic Optimization of Ensemble Neural Network Architectures for Prediction of COVID-19 Confirmed and Death Cases -- 1 Introduction -- 2 Basic Concepts -- 2.1 Artificial Neural Networks -- 2.2 Nonlinear Autoregressive Neural Network -- 2.3 Fuzzy Logic -- 2.4 Genetic Algorithms -- 3 Proposed Method -- 4 Results of the Experiment -- 4.1 Genetic Algorithms -- 5 Conclusions -- References -- Optimization of Modular Neural Networks for the Diagnosis of Cardiovascular Risk -- 1 Introduction -- 2 Literature Review -- 2.1 Flower Pollination Algorithm -- 2.2 Bird Swarm Algorithm -- 2.3 Blood Pressure and Hypertension -- 2.4 Cardiovascular Disease and Heart Age -- 2.5 Framingham Heart Study -- 3 Proposed Method -- 4 Results -- 5 Conclusions and Future Work -- References -- A Review on the Cuckoo Search Algorithm -- 1 Introduction -- 2 An Analogy with Nature -- 2.1 Cuckoo Search Algorithm -- 2.2 Algorithm Rules -- 2.3 Levy Flights -- 2.4 Mathematical Formulas -- 2.5 Flowchart CS -- 3 Implementation of Levy Flights in Other Algorithms -- 4 Variants of the Cuckoo Search Algorithm -- 5 Applications -- 6 Conclusions -- References -- An Improved Convolutional Neural Network Based on a Parameter Modification of the Convolution Layer -- 1 Introduction -- 2 Background and Basic Concepts -- 2.1 Convolutional Neural Network Concepts -- 2.2 Edge Detectors -- 2.3 Sobel Operator -- 2.4 Prewitt Operator -- 2.5 Laplacian Operator -- 3 Proposed Approach -- 3.1 Proposed Architecture -- 3.2 Convolution Kernel Initialization -- 4 Experiments -- 4.1 Case Study MNIST Handwritten Digits -- 4.2 Case Study MNIST American Sign Language -- 4.3 Case Study Mexican Sign Language Database -- 5 Conclusions.; References -- Parameter Optimization of a Convolutional Neural Network Using Particle Swarm Optimization -- 1 Introduction -- 2 Convolutional Neural Network -- 2.1 Input Layer -- 2.2 Convolution Layer -- 2.3 Non-linearity Layer -- 2.4 Pooling Layer -- 2.5 Classifier Layer -- 3 Particle Swarm Optimization -- 3.1 Global Best PSO -- 3.2 Local Best PSO -- 4 Proposed Method -- 4.1 Parameter Optimization of the CNN -- 4.2 CNN-PSO Optimization Process -- 5 Experiments and Results -- 5.1 Exploratory Experiment -- 5.2 American Sign Language Alphabet (ASL Alphabet) Experiment -- 5.3 American Sign Language MNIST Experiment -- 5.4 Analysis and Comparison of Results -- 6 Conclusion and Future Work -- References -- One-Dimensional Bin Packing Problem: An Experimental Study of Instances Difficulty and Algorithms Performance -- 1 Introduction -- 2 The Bin Packing ; Problem -- 2.1 Instances -- 2.2 Index Description -- 2.3 Performance Measures -- 3 Algorithms -- 3.1 First Fit Decreasing (FFD) -- 3.2 Best Fit Decreasing (BFD) -- 3.3 Minimum Bin Slack (MBS) -- 3.4 GGA-CGT -- 4 Results -- 5 Experimental Analysis -- 5.1 Class BPP.25 -- 5.2 Class BPP.5 -- 5.3 Class BPP.75 -- 5.4 Class BPP1 -- 6 Conclusions and Future Work -- References -- Looking for Emotions in Evolutionary Art -- 1 Introduction -- 2 In Search of Lost Emotions -- 2.1 Humans in the EA Loop -- 3 Methodology: Analysis of Emotions in the Era of Evolutionary Art -- 3.1 The Line -- 3.2 Simplifying the Problem -- 3.3 Evospace-Interactive Module -- 4 Results -- 4.1 Analyzing Formal Elements -- 4.2 Are Emotions Properly Understood? -- 4.3 Audience Analysis -- 4.4 International Art Competitions -- 5 Conclusion -- References -- Review of Hybrid Combinations of Metaheuristics for Problem Solving Optimization -- 1 Introduction -- 2 Review of Hybrid or Combined Metaheuristics -- 3 Discussion -- 4 Conclusions.; References -- GPU Accelerated Membrane Evolutionary Artificial Potential Field for Mobile Robot Path Planning -- 1 Introduction -- 2 Fundamentals -- 2.1 Membrane Computing -- 2.2 Evolutionary Computation -- 2.3 Artificial Potential Field Method -- 3 GPU Accelerated MemEAPF -- 4 Results -- 4.1 Path Planning Results -- 4.2 Performance Results -- 5 Conclusions -- References -- Optimization of the Internet Shopping Problem with Shipping Costs -- 1 Introduction -- 1.1 Definition of the Problem -- 2 The General Structure of the Memetic Algorithm -- 2.1 Selection by Tournament -- 2.2 Crossover Operator -- 2.3 Mutation Operator -- 2.4 Local Search -- 2.5 Memetic Algorithm (MAIShOP) -- 3 Computational Experiments -- 4 Conclusions -- References -- Multiobjective Algorithms Performance When Solving CEC09 Test Instances -- 1 Introduction -- 2 Multiobjective Optimization -- 3 CEC09 Test Functions -- 4 Multiobjective Optimization Algorithms -- 5 Performance Metrics of Multiobjective Optimization -- 6 Computational Experiments -- 7 Conclusion and Future Work -- References -- Analysis of the Efficient Frontier of the Portfolio Selection Problem Instance of the Mexican Capital Market -- 1 Introduction -- 2 Multiobjective Algorithms in Comparison -- 3 CellDE -- 4 GDE3 -- 5 IBEA -- 6 MOCell -- 7 NSGA-II -- 8 NSGA-III -- 9 OMOPSO -- 10 PAES -- 11 SPEA2 -- 12 Computational Experiments -- 13 Conclusions -- References -- Multi-objective Portfolio Optimization Problem with Trapezoidal Fuzzy Parameters -- 1 Introduction -- 2 Elements of Fuzzy Theory -- 2.1 Fuzzy Sets -- 2.2 Generalized Fuzzy Numbers -- 2.3 Addition Operator -- 2.4 Graded Mean Integration (GMI) -- 2.5 Order Relation in the Set of the Trapezoidal Fuzzy Numbers -- 2.6 Pareto Dominance -- 3 Multi-objective Portfolio Optimization Problem with Trapezoidal Fuzzy Parameters -- 4 Proposal Algorithm T-NSGA-II.; 4.1 Representation of the Solutions -- 4.2 Evaluating the Solutions -- 4.3 One-Point Crossover Operator -- 4.4 Uniform Mutation Operator -- 4.5 Initial Population -- 4.6 Population Sorting -- 4.7 No-Dominated Sorting -- 4.8 Calculating the Crowding Distance (Deb et al. 2000) -- 4.9 Calculating the Spatial Spread Deviation (SSD) (Santiago et al. 2019) -- 4.10 Pseudocode of the T-NSGA-II Algorithm -- 5 Proposed Strategy to Assess the Performance of Multi-objective Algorithms in the Fuzzy Trapezoidal Numbers Domain -- 6 Computational Experiments -- 7 Conclusions -- References -- A Study on the Use of Hyper-heuristics Based on Meta-Heuristics for Dynamic Optimization -- 1 Introduction -- 2 Background and Definitions -- 2.1 Dynamic Multi-objective Optimization Problem -- 2.2 Dynamic Multi-objective Evolutionary Algorithm -- 2.3 Hyper-heuristic -- 2.4 ; Indicators to Evaluate DMOEAs Performance Over DMOPs -- 3 Relevant Properties to Consider from DMOPs -- 3.1 Objective Function -- 3.2 Decision Variables -- 3.3 Constraints -- 4 Known Hyper-heuristic Approaches Towards Solving DOPs -- 5 Proposed Checklist and Design Guide for Dynamic Hyper-heuristics -- 6 Case Studies Using the Proposed Guide and Checklist -- 6.1 Case Study 1 -- 6.2 Case Study 2 -- 7 Conclusions and Future Work -- References -- On the Adequacy of a Takagi-Sugeno-Kang Protocol as an Empirical Identification Tool for Sigmoidal Allometries in Geometrical Space -- 1 Introduction -- 2 Methods -- 2.1 Model of Complex Allometry -- 2.2 TSK Fuzzy Model -- 2.3 Data -- 2.4 Reproducibility Assessment -- 2.5 TSK Identification Procedures -- 2.6 Piecewise-Linear Schemes -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- A New Hybrid Method Based on ACO and PSO with Fuzzy Dynamic Parameter Adaptation for Modular Neural Networks Optimization -- 1 Introduction -- 2 Proposed Method.; 2.1 Ant System and ACO Algorithm.