Sara Silva - Böcker

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.

In its lucky 12+1 edition, during April 7-9, 2010, the European Conference on Genetic Programming (EuroGP) travelled to its most easterly location so far, theEuropeanCityof Culture2010,Istanbul,Turkey.EuroGPisthe onlyconf- enceworldwideexclusivelydevotedtogeneticprogrammingandtheevolutionary generation of computer programs. For over a decade, genetic programming (GP) has been considered the new form of evolutionary computation. With nearly 7,000 articles in the online GP bibliography maintained by William B. Langdon, we can say that it is now a mature ?eld. EuroGP has contributed to the success of the ?eld substantially, by being a unique forum for expressing new ideas, meeting, and starting up collaborations. The wide rangeoftopics in this volume re?ectthecurrentstateof researchin the ?eld, including representations, theory, operators and analysis, novel m- els, performance enhancements, extensions of genetic programming,and various applications. The volume contains contributions in the following areas: - Understanding GP behavior andGP analysis include articles on cro- over operators and a new way of analyzing results.-GPperformance presents work on performance enhancements through phenotypic diversity, simpli?cation, ?tness and parallelism. - Novel models and their application present innovative approaches with arti?cial biochemical networks, genetic regulatory networks and geometric di?erential evolution. - Grammatical evolution introduces advances in crossover, mutation and phenotype-genotype maps in this relatively new area. - Machine learning and data mining include articles that present data miningormachinelearningsolutionsusingGPandalsocombinedatamining and machine learning with GP. - Applications rangefromsolvingdi?erentialequations,routingproblems to ?le type detection, object-oriented testing, agents. This year we received 48 submissions, of which 47 were sent to the reviewers.

This book constitutes the refereed proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011, held in Torino, Italy, in April 2011 co-located with the Evo* 2011 events. This 20 revised full papers presented together with 9 poster papers were carefully reviewed and selected from 59 submissions. The wide range of topics in this volume reflect the current state of research in the field, including representations, theory, novel operators and techniques, self organization, and 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.

This book constitutes the refereed proceedings of the International Conference on the Applications of Evolutionary Computation, EvoApplications 2012, held in Málaga, Spain, in April 2012, colocated with the Evo* 2012 events EuroGP, EvoCOP, EvoBIO, and EvoMUSART. The 54 revised full papers presented were carefully reviewed and selected from 90 submissions. EvoApplications 2012 consisted of the following 11 tracks: EvoCOMNET (nature-inspired techniques for telecommunication networks and other parrallel and distributed systems), EvoCOMPLEX (algorithms and complex systems), EvoFIN (evolutionary and natural computation in finance and economics), EvoGAMES (bio-inspired algorithms in games), EvoHOT (bio-inspired heuristics for design automation), EvoIASP (evolutionary computation in image analysis and signal processing), EvoNUM (bio-inspired algorithms for continuous parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for risk management, security and defense applications), EvoSTIM (nature-inspired techniques in scheduling, planning, and timetabling), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments).

This book brings together some of the most impactful researchers in the field of Genetic Programming (GP), each one working on unique and interesting intersections of theoretical development and practical applications of this evolutionary-based machine learning paradigm.