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Evolutionary Machine Design

Autor Nadia Nedjah, Luiza de Macedo Mourelle
en Limba Engleză Hardback – 7 apr 2005
In recent years, genetic programming has attracted many researcher's attention and so became a consolidated methodology to automatically create new competitive computer programs. Concise and efficient synthesis of a variety of systems has been generated by evolutionary computations. Evolvable hardware is a growing discipline. It allows one to evolve creative and novel hardware architectures given the expected input/output behaviour. There are two kinds of evolvable hardware: extrinsic and intrinsic. The former relies on a simulated evolutionary process to evaluate the characteristics of the evolved designs while the latter uses hardware itself to do so. Usually, reconfigurable hardware such FPGA and FPAA are exploited. One of the main problems that still faces researchers in the field of evolutionary machine design is the scalability. This book is devoted to reporting innovative and significant progress in automatic machine design. Theoretical as well as practical chapters are contemplated. The scalability problem in evolutionary machine designs is addresses. The content of this book is divided into two main parts: evolvable hardware and genetic programming; and evolutionary designs. In the following, we give a brief description of the main contribution of each of the included chapters.
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Specificații

ISBN-13: 9781594544057
ISBN-10: 1594544050
Pagini: 218
Ilustrații: Illustrations
Dimensiuni: 185 x 259 x 22 mm
Greutate: 0.7 kg
Ediția:New.
Editura: Nova Science Publishers Inc

Cuprins

Preface; Routine High-Return Human-Competitive Evolvable Hardware; Using Generative Representations to Evolve Robots; Intrinsic Evolutionary Design of Analogue Building Blocks for Fuzzy Logic Controllers; Improving the Search by Encoding Multiple Solutions in a Chromosome; Real-World Evolutionary Designs: Secure Evolvable Hardware for Public-Key Cryptosystems; Automated Discovery of Innovative Designs of Mechanical Components Using Evolutionary Multi-Objective Algorithms; Toward Efficient Topological Synthesis of Dynamic Systems Using Genetic Programming; The Role of Simulated Evolution in Bioinformatics; Index.