Bøger i Natural Computing Series serien

This book shows how runtime behavior can be analyzed in a rigorous way and for combinatorial optimization in particular. It presents well-known problems such as minimum spanning trees, shortest paths, maximum matching, and covering and scheduling problems.

During the first week of September 1999, the Second EvoNet Summer School on Theoretical Aspects of Evolutionary Computing was held at the Middelheim cam pus of the University of Antwerp, Belgium.

The book's contributing authors are among the top researchers in swarm intelligence. Introductory chapters deal with the biological foundations, optimization, swarm robotics, and applications in new-generation telecommunication networks, while the second part contains chapters on more specific topics of swarm intelligence research.

Details robustness, stability, and performance of Evolutionary Algorithms in dynamic environments

This text examines how multiobjective evolutionary algorithms and related techniques can be used to solve problems, particularly in the disciplines of science and engineering.

This comprehensive book gives an up-to-date survey of the relevant bioinspired computing research fields - such as evolutionary computation, artificial life, swarm intelligence and ant colony algorithms - and examines applications in art, music and design.

At the beginning of the 1990s research started in how to combine soft comput ing with reconfigurable hardware in a quite unique way. Evolvable hardware has become a part of the curriculum at some universi ties. Evolvable hardware is being commercialized and there are specialized conferences devoted to evolvable hardware.

Membrane computing is an unconventional model of computation associated with a new computing paradigm. it is a branch of natural computing inspired by the structure and functioning of the living cell and devises distributed parallel computing models in the form of membrane systems.

The 30 coherently written chapters by leading researchers presented in this anthology are devoted to basic results achieved in computational intelligence since 1997.

Presents new mathematical and computational models as well as statistical methods for the solution of fundamental problems in the biosciences. Describes how to find regularities among empirical data, as well as conceptual models and theories.

Mika Hirvensalo maps out the new multidisciplinary research area of quantum computing. The special style of presentation makes the theory of quantum computing accessible to a larger audience.

This book integrates two areas of computer science, namely data mining and evolutionary algorithms. The motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods which perform a global search in the space of candidate solutions.

This book covers the basic theory, practical details and advanced research of the implementation of evolutionary methods on physical substrates. The authors present an overview of the successes achieved, and the book will act as a point of reference for both academic and industrial researchers.

The authors stress the relative simplicity, efficiency, flexibility of use, and suitability of various approaches used to solve difficult optimization problems. This textbook is a suitable introduction for undergraduate and graduate students, researchers, and professionals in computer science, engineering, and logistics.

Membrane computing is a branch of natural computing which investigates computing models abstracted from the structure and functioning of living cells and from their interactions in tissues or higher-order biological structures.

This book is an introduction to relevant aspects of the foraging literature for algorithmic design, and an overview of key families of optimization algorithms that stem from a foraging metaphor.

The field of biologically inspired computation has coexisted with mainstream computing since the 1930s, and the pioneers in this area include Warren McCulloch, Walter Pitts, Robert Rosen, Otto Schmitt, Alan Turing, John von Neumann and Norbert Wiener.

This book introduces some key problems in bioinformatics, discusses the models used to formally describe these problems, and analyzes the algorithmic approaches used to solve them. This book presents formal models in a mathematically precise, yet intuitive manner.

This unique text seeks to automate the design of a data mining algorithm. It first overviews data mining and evolutionary algorithms then discusses the design of a new genetic programming system for automating the design of full rule induction algorithms.

Predicting the future for financial gain is a difficult, sometimes profitable activity. This book focuses on the application of biologically inspired algorithms (BIAs) to financial modelling. It explains computer trading on financial markets and the difficulties faced in financial market modelling.

This edited book reports on recent developments in the theory of evolutionary computation, or more generally the domain of randomized search heuristics.

The book is useful for graduate students, researchers in the field, and the general scientific reader who is interested in inherently interdisciplinary research at the intersections of computer science, biology, chemistry, physics, engineering and mathematics.

This introduction to the field of hyper-heuristics presents the required foundations and tools and illustrates some of their applications.

This book provides a broad overview of the entire field of DNA computation, tracing its history and development. It concludes by outlining the challenges currently faced by researchers in the field. This book will be a useful reference for researchers and students, as well as an accessible introduction for those new to the field.

The field of biologically inspired computation has coexisted with mainstream computing since the 1930s, and the pioneers in this area include Warren McCulloch, Walter Pitts, Robert Rosen, Otto Schmitt, Alan Turing, John von Neumann and Norbert Wiener.

The study of the genetic basis for evolution has flourished in this century, as well as our understanding of the evolvability and programmability of biological systems. and the interface with genetic algorithms and genetic and evolutionary programming.

Consequently, EC researchers are enthusiastic about applying their arsenal for the design and optimization of deep neural networks (DNN). This book brings together the recent progress in DL research where the focus is particularly on three sub-domains that integrate EC with DL: (1) EC for hyper-parameter optimization in DNN;

This book is an introduction to relevant aspects of the foraging literature for algorithmic design, and an overview of key families of optimization algorithms that stem from a foraging metaphor.

This book explains the theory and application of evolutionary computer vision, a new paradigm where challenging vision problems can be approached using the techniques of evolutionary computing.