Efrén Mezura-Montes – författare

AI-Powered Insurance is a comprehensive exploration of how Artificial Intelligence is revolutionizing the global insurance industry. This book brings together cutting-edge research and real-world case studies, covering AI's transformative impact across underwriting, risk management, customer experience, and policy personalization. It examines emerging technologies, ethical challenges, and security concerns while analyzing machine learning applications in insurance policy evaluation. From automation versus human expertise to bibliometric research trends, the book offers diverse perspectives from leading experts. An essential resource for researchers, practitioners, and policymakers, it charts the future of intelligent, data-driven insurance in an increasingly digital world.

This conference LNAI  14502 volume constitutes the workshop proceedings of 22nd Mexican International Conference on Artificial Intelligence, held in November 2023 in Mérida, Yucatán, México. The total of 34 papers presented in this volume was carefully reviewed and selected from 54 submissions.The proceedings of MICAI 2023 workshops are structured into three sections:– WILE 2023: 16th Workshop on Intelligent Learning Environments– HIS 2023: 16th Workshop of Hybrid Intelligent Systems– CIAPP 2023: 5th Workshop on New Trends in Computational Intelligence and Applications

This book constitutes the proceedings of the 16th Mexican Conference on Pattern Recognition, MCPR 2024, held in Xalapa, Mexico, during June 19–22, 2024, Proceedings. The 36 full papers were carefully reviewed and selected from 68 submissions.

This book constitutes the proceedings of several workshops which were held in conjunction with the MICAI 2025 International Workshops on Advances in Computational Intelligence, MICAI 2025, held in Guanajuato, Mexico, during November 3–7, 2025.The 59 revised full papers presented in this book were carefully reviewed and selected from 85 submissions. The first part contains 33 papers from the 18th workshop of Hybrid Intelligent Systems (HIS 2025) andthe first workshop on Evolutionary Computation and Swarm Intelligence (ECSI 2025). The second volume contains 26 papers from the 18th Workshop on Intelligent Learning Environments (WILE 2025), the 7th Workshop on New Trends in Computational Intelligence and Applications (CIAPP 2025) and the first workshop on Challenges in Holistic AI for Real-world Applications and Learning Systems (CHARAL 2025).

This book constitutes the proceedings of several workshops which were held in conjunction with the MICAI 2025 International Workshops on Advances in Computational Intelligence, MICAI 2025, held in Guanajuato, Mexico, during November 3–7, 2025.The 59 revised full papers presented in this book were carefully reviewed and selected from 85 submissions. The first part contains 33 papers from the 18th workshop of Hybrid Intelligent Systems (HIS 2025) andthe first workshop on Evolutionary Computation and Swarm Intelligence (ECSI 2025). The second volume contains 26 papers from the 18th Workshop on Intelligent Learning Environments (WILE 2025), the 7th Workshop on New Trends in Computational Intelligence and Applications (CIAPP 2025) and the first workshop on Challenges in Holistic AI for Real-world Applications and Learning Systems (CHARAL 2025).

Evolutionary algorithms (EAs), as well as other bio-inspired heuristics, are widely usedto solvenumericaloptimizationproblems.However,intheir or- inal versions, they are limited to unconstrained search spaces i.e they do not include a mechanism to incorporate feasibility information into the ?tness function. On the other hand, real-world problems usually have constraints in their models. Therefore, a considerable amount of research has been d- icated to design and implement constraint-handling techniques. The use of (exterior) penalty functions is one of the most popular methods to deal with constrained search spaces when using EAs. However, other alternative me- ods have been proposed such as: special encodings and operators, decoders, the use of multiobjective concepts, among others. An e?cient and adequate constraint-handling technique is a key element in the design of competitive evolutionary algorithms to solve complex op- mization problems. In this way, this subject deserves special research e?orts.After asuccessfulspecialsessiononconstraint-handlingtechniquesusedin evolutionary algorithms within the Congress on Evolutionary Computation (CEC) in 2007, and motivated by the kind invitation made by Dr. Janusz Kacprzyk, I decided to edit a book, with the aim of putting together recent studies on constrained numerical optimization using evolutionary algorithms and other bio-inspired approaches. The intended audience for this book comprises graduate students, prac- tionersandresearchersinterestedonalternativetechniquestosolvenumerical optimization problems in presence of constraints.

Evolutionary algorithms (EAs), as well as other bio-inspired heuristics, are widely usedto solvenumericaloptimizationproblems.However,intheir or- inal versions, they are limited to unconstrained search spaces i.e they do not include a mechanism to incorporate feasibility information into the ?tness function. On the other hand, real-world problems usually have constraints in their models. Therefore, a considerable amount of research has been d- icated to design and implement constraint-handling techniques. The use of (exterior) penalty functions is one of the most popular methods to deal with constrained search spaces when using EAs. However, other alternative me- ods have been proposed such as: special encodings and operators, decoders, the use of multiobjective concepts, among others. An e?cient and adequate constraint-handling technique is a key element in the design of competitive evolutionary algorithms to solve complex op- mization problems. In this way, this subject deserves special research e?orts.After asuccessfulspecialsessiononconstraint-handlingtechniquesusedin evolutionary algorithms within the Congress on Evolutionary Computation (CEC) in 2007, and motivated by the kind invitation made by Dr. Janusz Kacprzyk, I decided to edit a book, with the aim of putting together recent studies on constrained numerical optimization using evolutionary algorithms and other bio-inspired approaches. The intended audience for this book comprises graduate students, prac- tionersandresearchersinterestedonalternativetechniquestosolvenumerical optimization problems in presence of constraints.

This book aims to discuss the core and underlying principles and analysis of the different constraint handling approaches. The main emphasis of the book is on providing an enriched literature on mathematical modelling of the test as well as real-world problems with constraints, and further development of generalized constraint handling techniques.