Bücher der Reihe Natural Computing Series

This book introduces a multipath routing algorithm for packet-switched telecommunication networks based on techniques observed in bee colonies. The algorithm, BeeHive, represents an important step towards intelligent networks that optimally manage resources.

Tells us which problems modern heuristics are expected to perform well on, and which should be left to traditional optimization methods. This book also teaches us to systematically design the "right" modern heuristic for a particular problem by providing a coherent view on design elements and working principles.

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.

By adopting a complexity-theoretical perspective, he derives general limitations for black-box optimization, yielding lower bounds on the performance of evolutionary algorithms, and then develops general methods for deriving upper and lower bounds step by step.

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.

Evolutionary algorithms, such as evolution strategies, genetic algorithms, or evolutionary programming, have found broad acceptance in the last ten years. This monograph provides the framework and the first steps toward the theoretical analysis of Evolution Strategies (ES).

Despite decades of work in evolutionary algorithms, there remains an uncertainty as to the relative benefits and detriments of using recombination or mutation. It integrates important prior work and introduces new theoretical techniques for studying evolutionary algorithms.

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.

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.

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.

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.

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

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 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.

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.

Taking inspiration from self-awareness in humans, this book introduces the new notion of computational self-awareness as a fundamental concept for designing and operating computing systems.

It originated from computational neuroscience and machine learning but has, in recent years, spread dramatically, and has been introduced into a wide variety of fields, including complex systems science, physics, material science, biological science, quantum machine learning, optical communication systems, and robotics.

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

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.

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 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.

Taking inspiration from self-awareness in humans, this book introduces the new notion of computational self-awareness as a fundamental concept for designing and operating computing systems.

Offers a perspective on the research in nanoscale science, engineering and computing. This title covers topics of DNA self-assembly in two-dimensional arrays and three-dimensional structures, molecular motors, DNA word design, molecular electronics, gene assembly, surface layer protein assembly, and membrane computing.

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.

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.

Theoretical tools and insights from discrete mathematics, theoretical computer science, and topology now play essential roles in our understanding of vital biomolecular processes. It may also serve as a guide and supplement for graduate courses in mathematical biology or bioinformatics, introducing nontraditional aspects of mathematical biology.

Recent advances in general-purpose computing on graphics processing units (GPGPU) have opened up the possibility for parallel evolutionary algorithms. This book details this exciting new approach, with emphasis on solving practical problems.