Leonardo Vanneschi – författare

This textbook provides the reader with an essential understanding of computational methods for intelligent systems. These are defined as systems that can solve problems autonomously, in particular problems where algorithmic solutions are inconceivable for humans or not practically executable by computers. Despite the rapidly growing applications in this field, the book avoids application details, instead focusing on computational methods that equip the reader with the methodological tools and competencies necessary to tackle current and future complex applications. The book consists of two parts: computational intelligence methods for optimization, and machine learning. Part I begins with the concept of optimization, and introduces local search algorithms, genetic algorithms, and particle swarm optimization. Part II begins with an introduction to machine learning and covers several methods, many of which can be used as supervised learning algorithms, such as decision treelearning, artificial neural networks, genetic programming, Bayesian learning, support vector machines, and ensemble methods, plus a discussion of unsupervised learning.This textbook is written in a self-contained style, suitable for undergraduate or graduate students in computer science and engineering, and for self-study by researchers and practitioners.

This textbook provides the reader with an essential understanding of computational methods for intelligent systems. These are defined as systems that can solve problems autonomously, in particular problems where algorithmic solutions are inconceivable for humans or not practically executable by computers. Despite the rapidly growing applications in this field, the book avoids application details, instead focusing on computational methods that equip the reader with the methodological tools and competencies necessary to tackle current and future complex applications. The book consists of two parts: computational intelligence methods for optimization, and machine learning. Part I begins with the concept of optimization, and introduces local search algorithms, genetic algorithms, and particle swarm optimization. Part II begins with an introduction to machine learning and covers several methods, many of which can be used as supervised learning algorithms, such as decision treelearning, artificial neural networks, genetic programming, Bayesian learning, support vector machines, and ensemble methods, plus a discussion of unsupervised learning.This textbook is written in a self-contained style, suitable for undergraduate or graduate students in computer science and engineering, and for self-study by researchers and practitioners.

This book constitutes the refereed proceedings of the 16th Italian Workshop on Artificial Life and Evolutionary Computation, WIVACE 2022, held in Gaeta, Italy, during September 14–16, 2022. The 21 full papers and 3 short papers included in this book were carefully reviewed and selected from 45 submissions.

This book constitutes the refereed proceedings of the 10th European Conference on Genetic Programming, EuroGP 2007, held in Valencia, Spain in April 2007 colocated with EvoCOP 2007. The 21 revised plenary papers and 14 revised poster papers were carefully reviewed and selected from 71 submissions. The papers address fundamental and theoretical issues, along with a wide variety of papers dealing with different application areas.

The 11th European Conference on Genetic Programming, EuroGP 2008, took place in Naples, Italy from 26 to 28 March in the University of Naples Congress Centre with spectacular views over the Gulf of Naples. This volume contains the papers for the 21 oral presentations and 10 posters that were presented during this time. A diverse array of topics were covered re?ecting the current state of research in the ?eld of Genetic Programming, including the latest work on representations, theory, operators and analysis, evolvable hardware, agents and numerous applications. A rigorous, double-blind peer review process was employed, with each s- mission reviewed by at least three members of the international Program C- mittee. In total 61 papers were submitted this year, making an acceptance rate of 34% for full papers, and an overall acceptance rate of 51% including posters. S- mission of papers and the reviewing process were greatly assisted by the use of the MyReview management software originally developed by Philippe Rigaux, Bertrand Chardon and other colleagues from the Universit´e Paris-Sud Orsay, France. We are especially grateful to Marc Schoenauer from INRIA, France for managing this system. Reviewers were asked to nominate keywords specifying their area of expertise, and these keywords were matched to those selected by the authors of the submitted papers with the assistance of the optimal assignment feature of the conference management software.

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 10th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2012, held in Málaga, Spain, in April 2012 co-located with the Evo* 2012 events. The 15 revised full papers presented together with 8 poster papers were carefully reviewed and selected from numerous submissions. Computational Biology is a wide and varied discipline, incorporating aspects of statistical analysis, data structure and algorithm design, machine learning, and mathematical modeling toward the processing and improved understanding of biological data. Experimentalists now routinely generate new information on such a massive scale that the techniques of computer science are needed to establish any meaningful result. As a consequence, biologists now face the challenges of algorithmic complexity and tractability, and combinatorial explosion when conducting even basic analyses.