Artificial neural networks (ANNs) offer a general framework for representing non-linear mappings from several input variables to several output variables, and they can be considered as an extension of the many conventional mapping techniques. In addition to many considerations on their biological foundations and their really wide spectrum of applications, constructing appropriate ANNs can be seen as a really hard problem. A distinguished task in building ANNs is the tuning of a set of parameters known as weights. This will be the main focus of the present book. The trained ANNs can be...

Cellular Genetic Algorithms defines a new class of optimization algorithms based on the concepts of structured populations and Genetic Algorithms (GAs). The authors explain and demonstrate the validity of these cellular genetic algorithms throughout the book. This class of genetic algorithms is shown to produce impressive results on a whole range of domains, including complex problems that are epistatic, multi-modal, deceptive, discrete, continuous, multi-objective, and random in nature. The focus of this book is twofold. On the one hand, the authors present new algorithmic models and...

"Parallel Evolutionary Computation" focuses on the aspects related to the parallelization of evolutionary computations, such as parallel genetic operators, parallel fitness evaluation, distributed genetic algorithms, and parallel hardware implementations, as well as on their impact on several applications.
The book is divided into four parts. The first part deals with a clear software-like and algorithmic vision on parallel evolutionary optimizations. The second part is about hardware implementations of genetic algorithms, a valuable topic which is hard to find in the present...

Here, a team of international experts brings together core ideas for solving complex problems in optimization across a wide variety of real-world settings, including computer science, engineering, transportation, telecommunications, and bioinformatics.

Part One--covers methodologies for complex problem solving including genetic programming, neural networks, genetic algorithms, hybrid evolutionary algorithms, and more.

Part Two--delves into applications including DNA sequencing and reconstruction, location of...

Artificial neural networks (ANNs) offer a general framework for representing non-linear mappings from several input variables to several output variables, and they can be considered as an extension of the many conventional mapping techniques. In addition to many considerations on their biological foundations and their really wide spectrum of applications, constructing appropriate ANNs can be seen as a really hard problem. A distinguished task in building ANNs is the tuning of a set of parameters known as weights. This will be the main focus of the present book. The trained ANNs can be...

Cellular Genetic Algorithms defines a new class of optimization algorithms based on the concepts of structured populations and Genetic Algorithms (GAs). The authors explain and demonstrate the validity of these cellular genetic algorithms throughout the book. This class of genetic algorithms is shown to produce impressive results on a whole range of domains, including complex problems that are epistatic, multi-modal, deceptive, discrete, continuous, multi-objective, and random in nature. The focus of this book is twofold. On the one hand, the authors present new algorithmic models and...

This book is the result of several years of research trying to better characterize parallel genetic algorithms (pGAs) as a powerful tool for optimization, search, and learning. Readers can learn how to solve complex tasks by reducing their high computational times. Dealing with two scientific fields (parallelism and GAs) is always difficult, and the book seeks at gracefully introducing from basic concepts to advanced topics.

The presentation is structured in three parts. The first one is targeted to the algorithms themselves, discussing their components, the physical...

This book is the result of several years of research trying to better characterize parallel genetic algorithms (pGAs) as a powerful tool for optimization, search, and learning. Readers can learn how to solve complex tasks by reducing their high computational times. Dealing with two scientific fields (parallelism and GAs) is always difficult, and the book seeks at gracefully introducing from basic concepts to advanced topics.

The presentation is structured in three parts. The first one is targeted to the algorithms themselves, discussing their components, the physical...