Ricardo Soto
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View article: Binary Pufferfish Optimization Algorithm for Combinatorial Problems
Binary Pufferfish Optimization Algorithm for Combinatorial Problems Open
Metaheuristics are a fundament pillar of Industry 4.0, as they allow for complex optimization problems to be solved by finding good solutions in a reasonable amount of computational time. One category of important problems in modern indust…
View article: Low-Overhead Learning: Quantized Shallow Neural Networks at the Service of Genetic Algorithm Optimization
Low-Overhead Learning: Quantized Shallow Neural Networks at the Service of Genetic Algorithm Optimization Open
Online parameter tuning significantly enhances the performance of optimization algorithms by dynamically adjusting mutation and crossover rates. However, current approaches often suffer from high computational costs and limited adaptabilit…
View article: New Binary Reptile Search Algorithms for Binary Optimization Problems
New Binary Reptile Search Algorithms for Binary Optimization Problems Open
Binarizing continuous metaheuristics to solve challenging NP-hard binary optimization problems is a fundamental step in adapting continuous algorithms for discrete domains. Binary optimization problems, such as the Set Covering Problem and…
View article: An Experimental Study of Transfer Functions and Binarization Strategies in Binary Arithmetic Optimization Algorithms for the Set Covering Problem
An Experimental Study of Transfer Functions and Binarization Strategies in Binary Arithmetic Optimization Algorithms for the Set Covering Problem Open
Metaheuristics have proven to be effective in solving large-scale combinatorial problems by combining global exploration with local exploitation, all within a reasonably short time. The balance between these phases is crucial to avoid slow…
View article: Adaptive Optimization of a Dual Moving Average Strategy for Automated Cryptocurrency Trading
Adaptive Optimization of a Dual Moving Average Strategy for Automated Cryptocurrency Trading Open
In recent years, computational intelligence techniques have significantly contributed to the automation and optimization of trading strategies. Despite the increasing sophistication of predictive models, classical technical indicators such…
View article: Damage Detection on Real Bridges Using Machine Learning Techniques: A Systematic Review
Damage Detection on Real Bridges Using Machine Learning Techniques: A Systematic Review Open
Preventive maintenance efforts for bridge infrastructure have proven to mitigate early deterioration and reduce the probability of severe damage. Modern research has focused on the employment of online data directly collected within the st…
View article: Evolution and Trends of the Exploration–Exploitation Balance in Bio-Inspired Optimization Algorithms: A Bibliometric Analysis of Metaheuristics
Evolution and Trends of the Exploration–Exploitation Balance in Bio-Inspired Optimization Algorithms: A Bibliometric Analysis of Metaheuristics Open
The balance between exploration and exploitation is a fundamental element in the design and performance of bio-inspired optimization algorithms. However, to date, its conceptual evolution and its treatment in the scientific literature have…
View article: Binary Secretary Bird Optimization Algorithm for the Set Covering Problem
Binary Secretary Bird Optimization Algorithm for the Set Covering Problem Open
The Set Coverage Problem (SCP) is an important combinatorial optimization problem known to be NP-complete. The use of metaheuristics to solve the SCP includes different algorithms. In particular, binarization techniques have been explored …
View article: Binary Chaotic White Shark Optimizer for the Unicost Set Covering Problem
Binary Chaotic White Shark Optimizer for the Unicost Set Covering Problem Open
The Unicost Set Covering Problem (USCP), an NP-hard combinatorial optimization challenge, demands efficient methods to minimize the number of sets covering a universe. This study introduces a binary White Shark Optimizer (WSO) enhanced wit…
View article: Machine Learning and Metaheuristics Approach for Individual Credit Risk Assessment: A Systematic Literature Review
Machine Learning and Metaheuristics Approach for Individual Credit Risk Assessment: A Systematic Literature Review Open
Credit risk assessment plays a critical role in financial risk management, focusing on predicting borrower default to minimize losses and ensure compliance. This study systematically reviews 23 empirical articles published between 2019 and…
View article: Application of Metaheuristics for Optimizing Predictive Models in iHealth: A Case Study on Hypotension Prediction in Dialysis Patients
Application of Metaheuristics for Optimizing Predictive Models in iHealth: A Case Study on Hypotension Prediction in Dialysis Patients Open
Intradialytic hypotension (IDH) is a critical complication in patients with chronic kidney disease undergoing dialysis, affecting both patient safety and treatment efficacy. This study examines the application of advanced machine learning …
View article: Optimization of Convolutional Neural Networks With Multi-Objective Function Metaheuristics for Melanoma Detection
Optimization of Convolutional Neural Networks With Multi-Objective Function Metaheuristics for Melanoma Detection Open
Early and accurate detection of melanoma remains a critical challenge in medical imaging. Convolutional Neural Networks (CNNs) have demonstrated superior classification performance, often surpassing dermatologists in diagnostic accuracy. H…
View article: A Binary Chaotic White Shark Optimizer
A Binary Chaotic White Shark Optimizer Open
This research presents a novel hybrid approach, which combines the White Shark Optimizer (WSO) metaheuristic algorithm with chaotic maps integrated into the binarization process. Inspired by the predatory behavior of white sharks, WSO has …
View article: Enhancing the Efficiency of a Cybersecurity Operations Center Using Biomimetic Algorithms Empowered by Deep Q-Learning
Enhancing the Efficiency of a Cybersecurity Operations Center Using Biomimetic Algorithms Empowered by Deep Q-Learning Open
In the complex and dynamic landscape of cyber threats, organizations require sophisticated strategies for managing Cybersecurity Operations Centers and deploying Security Information and Event Management systems. Our study enhances these s…
View article: A Novel Approach to Combinatorial Problems: Binary Growth Optimizer Algorithm
A Novel Approach to Combinatorial Problems: Binary Growth Optimizer Algorithm Open
The set-covering problem aims to find the smallest possible set of subsets that cover all the elements of a larger set. The difficulty of solving the set-covering problem increases as the number of elements and sets grows, making it a comp…
View article: Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection
Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection Open
Stagnation at local optima represents a significant challenge in bio-inspired optimization algorithms, often leading to suboptimal solutions. This paper addresses this issue by proposing a hybrid model that combines the Orca predator algor…
View article: Enhancing the Efficiency of a Cyber SOC Using Biomimetic Algorithms Empowered by Deep Q–Learning
Enhancing the Efficiency of a Cyber SOC Using Biomimetic Algorithms Empowered by Deep Q–Learning Open
Given the landscape of intricate and constantly evolving cyber threats, organizations demand refined strategies to deploy a Security Information and Event Management to support the management of a Cyber Security Operations Center. The dyna…
View article: Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection
Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection Open
Stagnation at local optima represents a significant challenge in bio–inspired optimization algorithms, often leading to suboptimal solutions. This paper addresses this issue by proposing a hybrid model that combines the Orca Predator…
View article: Skin Cancer Detection and Classification Using Neural Network Algorithms: A Systematic Review
Skin Cancer Detection and Classification Using Neural Network Algorithms: A Systematic Review Open
In recent years, there has been growing interest in the use of computer-assisted technology for early detection of skin cancer through the analysis of dermatoscopic images. However, the accuracy illustrated behind the state-of-the-art appr…
View article: Challenging the Limits of Binarization: A New Scheme Selection Policy Using Reinforcement Learning Techniques for Binary Combinatorial Problem Solving
Challenging the Limits of Binarization: A New Scheme Selection Policy Using Reinforcement Learning Techniques for Binary Combinatorial Problem Solving Open
In this study, we introduce an innovative policy in the field of reinforcement learning, specifically designed as an action selection mechanism, and applied herein as a selector for binarization schemes. These schemes enable continuous met…
View article: Autonomous Parameter Balance in Population-Based Approaches: A Self-Adaptive Learning-Based Strategy
Autonomous Parameter Balance in Population-Based Approaches: A Self-Adaptive Learning-Based Strategy Open
Population-based metaheuristics can be seen as a set of agents that smartly explore the space of solutions of a given optimization problem. These agents are commonly governed by movement operators that decide how the exploration is driven.…
View article: Chaotic Binarization Schemes for Solving Combinatorial Optimization Problems Using Continuous Metaheuristics
Chaotic Binarization Schemes for Solving Combinatorial Optimization Problems Using Continuous Metaheuristics Open
Chaotic maps are sources of randomness formed by a set of rules and chaotic variables. They have been incorporated into metaheuristics because they improve the balance of exploration and exploitation, and with this, they allow one to obtai…
View article: Anomaly Detection on Bridges Using Deep Learning With Partial Training
Anomaly Detection on Bridges Using Deep Learning With Partial Training Open
Bridges are exposed daily to environmental and operational factors that may cause weariness, fatigue, and damage. Continuous structural health monitoring (SHM) has been crucial to ensuring public safety, preventing accidents, and avert cos…
View article: Feature Selection Problem and Metaheuristics: A Systematic Literature Review about Its Formulation, Evaluation and Applications
Feature Selection Problem and Metaheuristics: A Systematic Literature Review about Its Formulation, Evaluation and Applications Open
Feature selection is becoming a relevant problem within the field of machine learning. The feature selection problem focuses on the selection of the small, necessary, and sufficient subset of features that represent the general set of feat…
View article: Dynamic Population on Bio-Inspired Algorithms Using Machine Learning for Global Optimization
Dynamic Population on Bio-Inspired Algorithms Using Machine Learning for Global Optimization Open
In the optimization field, the ability to efficiently tackle complex and high-dimensional problems remains a persistent challenge. Metaheuristic algorithms, with a particular emphasis on their autonomous variants, are emerging as promising…
View article: B-PSA: A Binary Pendulum Search Algorithm for the Feature Selection Problem
B-PSA: A Binary Pendulum Search Algorithm for the Feature Selection Problem Open
The digitization of information and technological advancements have enabled us to gather vast amounts of data from various domains, including but not limited to medicine, commerce, and mining. Machine learning techniques use this informati…
View article: Binarization of Metaheuristics: Is the Transfer Function Really Important?
Binarization of Metaheuristics: Is the Transfer Function Really Important? Open
In this work, an approach is proposed to solve binary combinatorial problems using continuous metaheuristics. It focuses on the importance of binarization in the optimization process, as it can have a significant impact on the performance …
View article: A Learning—Based Particle Swarm Optimizer for Solving Mathematical Combinatorial Problems
A Learning—Based Particle Swarm Optimizer for Solving Mathematical Combinatorial Problems Open
This paper presents a set of adaptive parameter control methods through reinforcement learning for the particle swarm algorithm. The aim is to adjust the algorithm’s parameters during the run, to provide the metaheuristics with the ability…