Bernabe Dorronsoro – författare

Cellular Genetic Algorithms defines a new class of optimization algorithms based on the concepts of structured populations and Genetic Algorithms (GAs). The authors explain and demonstrate the validity of these cellular genetic algorithms throughout the book. This class of genetic algorithms is shown to produce impressive results on a whole range of domains, including complex problems that are epistatic, multi-modal, deceptive, discrete, continuous, multi-objective, and random in nature. The focus of this book is twofold. On the one hand, the authors present new algorithmic models and extensions to the basic class of Cellular GAs in order to tackle complex problems more efficiently. On the other hand, practical real world tasks are successfully faced by applying Cellular GA methodologies to produce workable solutions of real-world applications. These methods can include local search (memetic algorithms), cooperation, parallelism, multi-objective, estimations of distributions, and self-adaptive ideas to extend their applicability.The methods are benchmarked against well-known metaheuristics like Genetic Algorithms, Tabu Search, heterogeneous GAs, Estimation of Distribution Algorithms, etc. Also, a publicly available software tool is offered to reduce the learning curve in applying these techniques. The three final chapters will use the classic problem of “vehicle routing” and the hot topics of “ad-hoc mobile networks” and “DNA genome sequencing” to clearly illustrate and demonstrate the power and utility of these algorithms.

Describes how evolutionary algorithms (EAs) can be used to identify, model, and minimize day-to-day problems that arise for researchers in optimization and mobile networkingMobile ad hoc networks (MANETs), vehicular networks (VANETs), sensor networks (SNs), and hybrid networks—each of these require a designer’s keen sense and knowledge of evolutionary algorithms in order to help with the common issues that plague professionals involved in optimization and mobile networking.This book introduces readers to both mobile ad hoc networks and evolutionary algorithms, presenting basic concepts as well as detailed descriptions of each. It demonstrates how metaheuristics and evolutionary algorithms (EAs) can be used to help provide low-cost operations in the optimization process—allowing designers to put some “intelligence” or sophistication into the design. It also offers efficient and accurate information on dissemination algorithms, topology management, and mobility models to address challenges in the field.Evolutionary Algorithms for Mobile Ad Hoc Networks: Instructs on how to identify, model, and optimize solutions to problems that arise in daily researchPresents complete and up-to-date surveys on topics like network and mobility simulatorsProvides sample problems along with solutions/descriptions used to solve each, with performance comparisonsCovers current, relevant issues in mobile networks, like energy use, broadcasting performance, device mobility, and moreEvolutionary Algorithms for Mobile Ad Hoc Networks is an ideal book for researchers and students involved in mobile networks, optimization, advanced search techniques, and multi-objective optimization.

Cellular Genetic Algorithms defines a new class of optimization algorithms based on the concepts of structured populations and Genetic Algorithms (GAs). The authors explain and demonstrate the validity of these cellular genetic algorithms throughout the book. This class of genetic algorithms is shown to produce impressive results on a whole range of domains, including complex problems that are epistatic, multi-modal, deceptive, discrete, continuous, multi-objective, and random in nature. The focus of this book is twofold. On the one hand, the authors present new algorithmic models and extensions to the basic class of Cellular GAs in order to tackle complex problems more efficiently. On the other hand, practical real world tasks are successfully faced by applying Cellular GA methodologies to produce workable solutions of real-world applications. These methods can include local search (memetic algorithms), cooperation, parallelism, multi-objective, estimations of distributions, and self-adaptive ideas to extend their applicability.The methods are benchmarked against well-known metaheuristics like Genetic Algorithms, Tabu Search, heterogeneous GAs, Estimation of Distribution Algorithms, etc. Also, a publicly available software tool is offered to reduce the learning curve in applying these techniques. The three final chapters will use the classic problem of “vehicle routing” and the hot topics of “ad-hoc mobile networks” and “DNA genome sequencing” to clearly illustrate and demonstrate the power and utility of these algorithms.

This volume constitutes the refereed proceedings of the Third International Conference on Optimization and Learning, OLA 2020, held in Cádiz, Spain, in February 2020.

This volume constitutes the refereed proceedings of the 4th International Conference on Optimization and Learning, OLA 2021, held in Catania, Italy, in June 2021. The papers presented in the volume are organized in topical sections on ?synergies between optimization and learning; machine learning and deep learning;

This volume constitutes selected papers presented during the 8th International Conference on Metaheuristics and Nature Inspired Computing, META 2021, held in Marrakech, Morocco, in October 201. Due to the COVID-19 pandemic the conference was partiqally held online. They are organized in the topical sections on ?combinatorial optimization;

This book constitutes the refereed proceedings of the 5th International Conference on Optimization and Learning, OLA 2022, which took place in Syracuse, Sicilia, Italy, in July 2022. The papers are organized in the following topical sections: Optimization and Learning;

This book constitutes the refereed proceedings of the 6th International Conference on Optimization and Learning, OLA 2023, held in Malaga, Spain, during May 3–5, 2023. They were organized in topical sections as follows: advanced optimization; learning methods to enhance optimization tools; optimization applied to learning methods;

In the age of transformative artificial intelligence (AI), which has the potential to revolutionize our lives, this book provides a comprehensive exploration of successful research and applications in AI and data analytics.Covering innovative approaches, advanced algorithms, and data analysis methodologies, this book addresses complex problems across topics such as machine learning, pattern recognition, data mining, optimization, and predictive modeling.With clear explanations, practical examples, and cutting-edge research, this book seeks to expand the understanding of a wide readership, including students, researchers, practitioners, and technology enthusiasts eager to explore these exciting fields.Featuring real-world applications in education, health care, climate modeling, cybersecurity, smart transportation, conversational systems, and material analysis, among others, this book highlights how these technologies can drive innovation and generate competitive advantages.

In the age of transformative artificial intelligence (AI), which has the potential to revolutionize our lives, this book provides a comprehensive exploration of successful research and applications in AI and data analytics.Covering innovative approaches, advanced algorithms, and data analysis methodologies, this book addresses complex problems across topics such as machine learning, pattern recognition, data mining, optimization, and predictive modeling.With clear explanations, practical examples, and cutting-edge research, this book seeks to expand the understanding of a wide readership, including students, researchers, practitioners, and technology enthusiasts eager to explore these exciting fields.Featuring real-world applications in education, health care, climate modeling, cybersecurity, smart transportation, conversational systems, and material analysis, among others, this book highlights how these technologies can drive innovation and generate competitive advantages.

This book presents numerous successful applications of AI in various contexts.The innovative implications covered fall under the general field of machine learning (ML), including deep learning, decision-making, forecasting, pattern recognition, information retrieval, and interpretable AI.

This book presents numerous successful applications of AI in various contexts.The innovative implications covered fall under the general field of machine learning (ML), including deep learning, decision-making, forecasting, pattern recognition, information retrieval, and interpretable AI.

This book constitutes the refereed proceedings of the 9th International Conference on Metaheuristics and Nature Inspired Computing, META 2023, held in Marrakech, Morocco, during November 1-4, 2023. The papers are divided into the following topical sections: combinatorial optimization;

This book constitutes the refereed proceedings of the 7th International Conference on Optimization and Learning, OLA 2024, held in Dubrovnik, Croatia, during May 13–15, 2024. The 24 full papers presented here were carefully reviewed and selected from 64 submissions.

This book constitutes the refereed proceedings of the 8th International Conference on Optimization and Learning, OLA 2025, held in Dubai, United Arab Emirates, during April 23–25, 2025.The 30 full papers presented here were carefully reviewed and selected from 70 submissions. They were organized in the following topical sections: Soft Computing and Machine Learning Advanced Models; Optimization and Search Algorithms: Metaheuristics and Hybrid Approaches; Modelling, Simulation and Complex Systems; Applications in Industry and Cibersecurity.