José Carlos Ortiz-Bayliss

Autism Spectrum Disorder is a long-term neurodevelopmental disorder. Early diagnosis is crucial for timely rehabilitation and intervention. Recently, machine learning and deep learning techniques have been widely explored and have produced encouraging results using eye-tracking scanpath images for the early detection of ASD. However, these approaches exhibit inconsistent performance and classification error rates, as well as limited generalization, due to differences in learning approaches and architectural designs across individual models. To address these problems, we employed a weighted-average ensemble of deep learning models using eye-tracking scanpath images. In this work, two different pretrained convolutional neural networks were selected, including Xception and VGG16, based on their proven efficacy and performance. Moreover, we fine-tuned each model individually and evaluated them on a dataset containing eye-tracking scanpath images. We implemented a weighted-average ensemble technique to combine the diverse predictions of the two models. It reduces classification errors and improves the model’s generalization and overall performance. The adopted weighted-average ensemble technique achieved an accuracy of 98.18%, with a perfect recall, and a competitive Area Under the Curve (AUC) of 99.59%. These findings highlight that applying a weighted average to integrate multiple models’ predictions strengthens the generalization and reliability of ASD detection.

This work introduces an alternative approach that utilizes a Hyper-Heuristic framework under the umbrella of Automated Algorithm Design to dynamically tailor Metaheuristics (MHs) for fine-tuning PID controller parameters. Our findings emphasize the robust consistency, high precision, and adaptability of the method in creating custom, metaphor-free MHs. We employ this methodology in a case study of real-world engineering problems to develop a control-heuristic-based tuner that significantly improves the performance of DC-DC Buck converters across diverse operational conditions. The analysis reveals dominant trends in search operator preferences, highlighting the reliance on genetic crossover and swarm dynamics. These insights provide actionable directions for similar applications in power electronics. Through rigorous statistical comparisons, we verify the superior efficacy and reliability of our approach relative to conventional methods, remarking on its potential for broad application in the field.

This study investigates and compares different molecular mutation strategies to optimize their application as genetic algorithm operators in drug design. We evaluated five distinct mutation methods—Graph-Based Genetic Algorithm, Graph-Based Generative Model, SmilesClickChem, SELFIES Token, and SMILES Token Mutation—by assessing their computational efficiency, validity, and impact on molecular complexity and structural conservation. Our results reveal that the Graph-Based Genetic Algorithm achieves the highest molecular validity (96.5%) while maintaining computational efficiency, making it ideal for rapid iterative drug discovery. SmilesClickChem and Graph-Based Generative Model tend to increase molecular complexity, whereas SF-T simplifies molecular structures, suggesting different applications in lead optimization. Additionally, we analyzed mutation-induced changes in pIC50 potency and found that SELFIES Token caused the most substantial shifts in bioactivity, particularly in SRC-targeted molecules. These findings underscore the importance of selecting the appropriate mutation strategy to balance validity, structural diversity, and computational cost in AI-driven drug design. Our insights help refine evolutionary algorithms for molecular generation and optimize candidate selection in early-stage drug discovery.

Hyper-heuristics, or simply heuristics to choose heuristics, represent a powerful approach to tackling complex optimization problems. These methods decide which heuristic to apply throughout the solving process, aiming to improve the solving process. While they have demonstrated significant success across various domains, their suitability for all problem instances, even within a specific domain, is not guaranteed. The literature provides many examples of successful hyper-heuristic models for packing problems. Among those models, we can mention rule-based and fixed-sequence-based hyper-heuristics. These two models have proven useful in various scenarios. This paper investigates a genetic-based approach that produces hybrid hyper-heuristics. Such hybrid hyper-heuristics combine rule-based decisions while firing heuristic sequences. The rationale behind this hybrid approach is that we aimed to combine the strengths of both approaches. Although we expected to improve on the individual performance of the methods, we obtained contradictory results that suggest that, at least in this work, combining the strengths of different hyper-heuristic models may not be a suitable approach.

Autism Spectrum Disorder (ASD) encompasses various neurological disorders with symptoms varying by age, development, genetics, and other factors. Core symptoms include decreased pain sensitivity, difficulty sustaining eye contact, incorrect auditory responses, and social engagement issues. Diagnosing ASD poses challenges as signs can appear at early stages of life, leading to delayed diagnoses. Traditional diagnosis relies mainly on clinical observation, which is a subjective and time-consuming approach. However, AI-driven techniques, primarily those within machine learning and deep learning, are becoming increasingly prevalent for the efficient and objective detection and classification of ASD. In this work, we review and discuss the most relevant related literature between January 2016 and May 2024 by focusing on ASD detection or classification using diverse technologies, including magnetic resonance imaging, facial images, questionnaires, electroencephalogram, and eye tracking data. Our analysis encompasses works from major research repositories, including WoS, PubMed, Scopus, and IEEE. We discuss rehabilitation techniques, the structure of public and private datasets, and the challenges of automated ASD detection, classification, and therapy by highlighting emerging trends, gaps, and future research directions. Among the most interesting findings of this review are the relevance of questionnaires and genetics in the early detection of ASD, as well as the prevalence of datasets that are biased toward specific genders, ethnicities, or geographic locations, restricting their applicability. This document serves as a comprehensive resource for researchers, clinicians, and stakeholders, promoting a deeper understanding and advancement of AI applications in the evaluation and management of ASD.

Hyper-heuristics, or simply heuristics to choose heuristics, represent a powerful approach to tackling complex optimization problems. These methods decide which heuristic to apply throughout the solving process, aiming to improve the solving process. While they have demonstrated significant success across various domains, their suitability for all problem instances, even within a specific domain, is not guaranteed. The literature provides many examples of successful hyper-heuristic models for packing problems. Among those models, we can mention rule-based and fixed-sequence-based hyper-heuristics. These two models have proven useful in various scenarios. This paper investigates a genetic-based approach that produces hybrid hyper-heuristics. Such hybrid hyper-heuristics combine rule-based decisions while firing heuristic sequences. The rationale behind this hybrid approach is that we aimed to combine the strengths of both approaches. Although we expected to improve on the individual performance of the methods, we obtained unexpected results.