In this book, recent theoretical developments on fuzzy logic, neural networks and optimization algorithms, as well as their hybrid combinations, are presented. In addition, the above-mentioned methods are presented in application areas such as, intelligent control and robotics, pattern recognition, medical diagnosis, decision-making, time series prediction and optimization of complex problems. The book contains a collection of papers focused on hybrid intelligent systems based on soft computing techniques. There are a group of papers with the main theme of type-1 and type-2 fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on type-1 and type-2 fuzzy logic and their applications. There also a group of papers that offer theoretical concepts and applications of meta-heuristics in different areas. Another group of papers outlines diverse applications of fuzzy logic, neural networks and hybrid intelligent systems in medical problems. There are also some papers that present theory and practice of neural networks in different application areas. In addition, there are papers that offer theory and practice of optimization and evolutionary algorithms in different application areas. Finally, there are a group of papers describing applications of fuzzy logic, neural networks and meta-heuristics in pattern recognition and classification problems.
The experiments were performed using the Python programming language. The TensorFlow framework and the Keras API are implemented in the generation and training of the models to analyze the impact of various image preprocessing techniques. The experiments were performed independently of each other for each of the previously defined filters. The experiments were carried out on a machine with the following specifications: an Intel core i7-11800H processor, 32 GB of DDR4 3200 MHz RAM, and an Nvidia RTX 3080 Laptop edition GPU with 16 GB of video memory running Ubuntu 20.04 LTS.
This section describes the experiments performed to obtain the solutions via the AutoML process using PSO as the optimization engine. Provided that PSO, as any other metaheuristic is a semi-stochastic method which depends on initial solutions and other factors, the AutoML solutions found may vary between different executions of this process. Because of this, the AutoML process is executed 10 times, to generate 10 different “optimal” solutions. After the AutoML process, the 10 solutions returned are compared with each other by performing 33 trials, each consisting in re-training and evaluating their performance in forecasting of the stock prices. This multi-trial evaluation is performed to provide statistical support to our final conclusions. To perform the AutoML process the PSO algorithm and the rest of the system was implemented using several Python libraries including NumPy, Pandas, Scikit-Learn and TensorFlow. The metaheuristic iteratively performs feature selection and design of the LSTM network with the objective of finding a configuration that minimizes the error of the network for the forecasting of the closing price of Google’s stocks.
Author(s): Oscar Castillo; Patricia Melin
Series: Studies in Computational Intelligence
Publisher: Springer Nature Switzerland
Year: 2023
Language: English
Pages: 489
Contents
Neural Networks
A Decision-Making Approach Based on Multiple Neural Networks for Clustering and Prediction of Time Series
Martha Ramirez and Patricia Melin
Approximation of Physicochemical Properties Based on a Message Passing Neural Network Approach
Leonardo Velazquez-Ruiz, Graciela Ramirez-Alonso, Fernando Gaxiola, Javier Camarillo-Cisneros, Daniel Espinobarro and Alain Manzo-Martinez
Convolutional Neural Networks for Multiclass Classification of Masks
Alexis Campos, Patricia Melin and Daniela Sánchez
Quanvolutional Neural Network Applied to MNIST
Daniel Alejandro Lopez, Oscar Montiel, Miguel Lopez-Montiel, Moisés Sánchez-Adame and Oscar Castillo
Traffic Sign Recognition Using Fuzzy Preprocessing and Deep Neural Networks
Cesar Torres, Claudia I. Gonzalez and Gabriela E. Martinez
Optimization
Fuzzy Dynamic Adaptation of an Artificial Fish Swarm Algorithm for the Optimization of Benchmark Functions
Leticia Amador-Angulo, Patricia Ochoa, Cinthia Peraza and Oscar Castillo
Particle Swarm Optimization Algorithm with Improved Opposition-Based Learning (IOBL-PSO) to Solve Continuous Problems
Miguel Á. García-Morales, Héctor J. Fraire-Huacuja, José A. Brambila-Hernández, Juan Frausto-Solís, Laura Cruz-Reyes, Claudia G. Gómez-Santillán and Juan M. Carpio-Valadez
Study on the Effect of Chaotic Maps in the Formation of New Universes in the Multiverse Optimizer Algorithm
Lucio Amézquita, Oscar Castillo, José Soria and Prometeo Cortes-Antonio
Performance Comparative of Surrogate Models as Fitness Functions for Metaheuristic Algorithms
David Bolaños-Rojas, Jorge A. Soria-Alcaraz, Andrés Espinal and Marco A. Sotelo-Figueroa
A New Continuous Mycorrhiza Optimization Nature-Inspired Algorithm
Hector Carreon-Ortiz, Fevrier Valdez and Oscar Castillo
Optimal Tuning of an Active Disturbance Rejection Controller Using a Particle Swarm Optimization Algorithm
Olga L. Jiménez Morales, Diego Tristán Rodríguez, Rubén Garrido and Efrén Mezura-Montes
Fuzzy Logic
Optimization of Fuzzy Controllers Using Distributed Bioinspired Methods with Random Parameters
Alejandra Mancilla, Oscar Castillo and Mario García-Valdez
Application of Compensatory Fuzzy Logic in Diabetes Problem Using Pima-Indians Dataset
José Fernando Padrón-Tristán, Laura Cruz-Reyes, Rafael A. Espin-Andrade, Claudia Guadalupe Gómez Santillán and Carlos Eric Llorente-Peralta
Comparison of the Effect of Parameter Adaptation in Bio-inspired CS Algorithm Using Type-2 Fuzzy Logic
Maribel Guerrero, Fevrier Valdez and Oscar Castillo
Interpretability of an Archimedean Compensatory Fuzzy Logic in Data Analytics: Some Case Studies
Carlos Eric Llorente-Peralta, Laura Cruz-Reyes, Rafael Alejandro Espín-Andrade and José Fernando Padron-Tristan
A New Selection and Class Prediction Using Type-1 Fuzzy Logic Applied to a Convolutional Neural Network
Yutzil Poma and Patricia Melin
Relaxed Differential Evolution Algorithm
Prometeo Cortés-Antonio, Arturo Téllez-Velázquez, Raúl Cruz-Barbosa and Oscar Castillo
Optimization: Theory and Applications
Automatic Characterization of Time Series Using Metaheuristic Algorithms for Epidemics Spread Analysis
Valentín Calzada-Ledesma and Andrés Espinal
Comparative Study of Heuristics for the One-Dimensional Bin Packing Problem
Jessica González-San-Martín, Laura Cruz-Reyes, Claudia Gómez-Santillán, Héctor Fraire, Nelson Rangel-Valdez, Bernabé Dorronsoro and Marcela Quiroz-Castellanos
Experimental Evaluation of Adaptive Operators Selection Methods for the Dynamic Multiobjective Evolutionary Algorithm Based on Decomposition (DMOEA/D)
José A. Brambila-Hernández, Miguel Á. García-Morales, Héctor J. Fraire-Huacuja, Armando Becerra del Angel, Eduardo Villegas-Huerta and Ricardo Carbajal-López
Automated Machine Learning to Improve Stock-Market Forecasting Using PSO and LSTM Networks
Francisco J. Pedroza-Castro, Alfonso Rojas-Domínguez and Martín Carpio
Evolutionary Gaussian-Gradient: A New Optimization Algorithm for the Electromechanical Design of Gravitational Batteries
Juan de Anda-Suárez, Felipe J. Flores-Calva, Daniel Jiménez-Mendoza and Germán Pérez-Zúñiga
A Comparison Between Selection Operators Heuristics of Perturbation in CSP
Lucero Ortiz-Aguilar, Hernández-Aguirre Yeovanna, M. Benitez, Sergio Rodriguez-Miranda and Fernando Mendoza-Vazquez
Hybrid Intelligent Systems
Trajectory Tracking Control of Wheeled Mobile Robots Using Neural Networks and Feedback Control Techniques
Victor D. Cruz, Jesus A. Rodriguez, Luis T. Aguilar and Roger Miranda Colorado
An Evolutionary Bilevel Optimization Approach for Neuroevolution
Rocío Salinas-Guerra, Jesús-Adolfo Mejía-Dios, Efrén Mezura-Montes and Aldo Márquez-Grajales
Recovering from Population Extinction in the Animal Life Cycle Algorithm (ALCA)
J. C. Felix-Saul and Mario Garcia Valdez
Multi-objective Optimization Through Coevolution and Outranking Methods with Uncertainty Management
Lorena Rosas-Solórzano, Claudia Gomez-Santillan, Nelson Rangel-Valdez, Eduardo Fernández, Laura Cruz-Reyes, Lucila Morales-Rodriguez and Hector Fraire-Huacuja
Experimental Proposal with Mallows Distribution Applied to the Mixed No-Idle Permutation Flowshop Scheduling Problem
E. M. Sánchez Márquez, M. Ornelas-Rodríguez, H. J. Puga-Soberanes, Pérez-Rodríguez, Ricardo and Martin Carpio
Interval Type-3 Fuzzy Decision Making in Material Surface Quality Control
Oscar Castillo and Patricia Melin
Interval Type-3 Fuzzy Decision Making in Quality Evaluation for Speaker Manufacturing
