Alberto Moraglio – författare

This volume constitutes the proceedings of the First International Workshop on Quantum Computing and Artificial Intelligence, QC+AI 2025, held in Philadelphia, PA, USA, during March 2025.The 7 full papers presented in this volume were carefully reviewed and selected from 19 submissions.This conference focusses on the application of quantum computing for artificial intelligence tasks, with a strong emphasis on quantum neural networks.

This book constitutes the proceedings of the Second International Workshop on Quantum Computing and Artificial Intelligence, QC+AI 2026, which took place in Singapore on January 27, 2026.The 7 full papers included in this book were carefully reviewed and selected from 17 submissions. They deal with up to date topics in quantum optimization and quantum machine learning.

This book constitutes the refereed proceedings of the 11th European Conference on Genetic Programming, EuroGP 2009, held in Tübingen, Germany, in April 2009 colocated with the Evo* 2009 events. The 21 revised plenary papers and 9 revised poster papers were carefully reviewed and selected from a total of 57 submissions. A great variety of topics are presented reflecting the current state of research in the field of genetic programming, including the latest work on representations, theory, operators and analysis, feature selection, generalisation, coevolution and numerous applications.

This book constitutes the refereed proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012, held in Málaga, Spain, in April 2012 co-located with the Evo* 2012 events. The 18 revised full papers presented together with 5 poster papers were carefully reviewed and selected from 46 submissions. The wide range of topics in this volume reflects the current state of research in the field, including different genres of GP (tree-based, grammar-based, Cartesian), theory, novel operators, and applications.

Metaheuristics, and evolutionary algorithms in particular, are known to provide efficient, adaptable solutions for many real-world problems, but the often informal way in which they are defined and applied has led to misconceptions, and even successful applications are sometimes the outcome of trial and error. Ideally, theoretical studies should explain when and why metaheuristics work, but the challenge is huge: mathematical analysis requires significant effort even for simple scenarios and real-life problems are usually quite complex.  In this book the editors establish a bridge between theory and practice, presenting principled methods that incorporate problem knowledge in evolutionary algorithms and other metaheuristics. The book consists of 11 chapters dealing with the following topics: theoretical results that show what is not possible, an assessment of unsuccessful lines of empirical research; methods for rigorously defining the appropriate scope of problems while acknowledging the compromise between the class of problems to which a search algorithm is applied and its overall expected performance; the top-down principled design of search algorithms, in particular showing that it is possible to design algorithms that are provably good for some rigorously defined classes; and, finally, principled practice, that is reasoned and systematic approaches to setting up experiments, metaheuristic adaptation to specific problems, and setting parameters. With contributions by some of the leading researchers in this domain, this book will be of significant value to scientists, practitioners, and graduate students in the areas of evolutionary computing, metaheuristics, and computational intelligence.

This book constitutes the refereed proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013, held in Vienna, Austria, in April 2013 co-located with the Evo* 2013 events, EvoMUSART, EvoCOP, EvoBIO, and EvoApplications. The 18 revised full papers presented together with 5 poster papers were carefully reviewed and selected from 47 submissions. The wide range of topics in this volume reflects the current state of research in the field, including different genres of GP (tree-based, linear, grammar-based, Cartesian), theory, novel operators, and applications.

Metaheuristics, and evolutionary algorithms in particular, are known to provide efficient, adaptable solutions for many real-world problems, but the often informal way in which they are defined and applied has led to misconceptions, and even successful applications are sometimes the outcome of trial and error. Ideally, theoretical studies should explain when and why metaheuristics work, but the challenge is huge: mathematical analysis requires significant effort even for simple scenarios and real-life problems are usually quite complex.  In this book the editors establish a bridge between theory and practice, presenting principled methods that incorporate problem knowledge in evolutionary algorithms and other metaheuristics. The book consists of 11 chapters dealing with the following topics: theoretical results that show what is not possible, an assessment of unsuccessful lines of empirical research; methods for rigorously defining the appropriate scope of problems while acknowledging the compromise between the class of problems to which a search algorithm is applied and its overall expected performance; the top-down principled design of search algorithms, in particular showing that it is possible to design algorithms that are provably good for some rigorously defined classes; and, finally, principled practice, that is reasoned and systematic approaches to setting up experiments, metaheuristic adaptation to specific problems, and setting parameters. With contributions by some of the leading researchers in this domain, this book will be of significant value to scientists, practitioners, and graduate students in the areas of evolutionary computing, metaheuristics, and computational intelligence.