Bücher der Reihe Natural Computing Series

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.

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

The editors, both leading experts in this field, have assembled a team of researchers to contribute 21 chapters organized into parts on simulated annealing, tabu search, ant colony algorithms, general purpose studies of evolutionary algorithms, applications of evolutionary algorithms, and metaheuristics.

Massively Parallel Evolutionary Computation on GPGPUs

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.

This volume refers to the formal description of mobility in computer science, using p-calculus, ambient calculus, bioambients, brane calculi, and systems of mobile membranes. Concepts are supported by examples and exercises, which makes it suitable for relevant courses.

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 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.

The editors, both leading experts in this field, have assembled a team of researchers to contribute 21 chapters organized into parts on simulated annealing, tabu search, ant colony algorithms, general purpose studies of evolutionary algorithms, applications of evolutionary algorithms, and metaheuristics.

Cartesian Genetic Programming (CGP) is a highly effective and increasingly popular form of genetic programming.

The natural world is full of wonderful examples of its successes, from engineering design feats such as powered flight, to the design of complex optical systems such as the mammalian eye, to the merely stunningly beautiful designs of orchids or birds of paradise.

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

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.

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.

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.

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.