Book Description:
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 scalibility. This book is devoted to reporting innovative and significant progress in automatic machine design. Theoretical as well as practical chapters are contemplated. The scalibility 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.

Table of Contents:
Chapter Availability

Individual chapters are available for $25 each by sending an email to novascience@earthlink.net. Nova will provide the chapter for your easy downloading or send it as an email attachment if you prefer.

Preface; Chapter 1: Routine High-Return Human-Competitive Evolvable Hardware (John R. Koza, Martin A. Keane and Matthew J. Streeter); Chapter 2: Using Generative Representations to Evolve Robots (Gregory S. Hornby); Chapter 3: Intrinsic Evolutionary Design of Analog Building Blocks for Fuzzy Logic Controllers (José Franco M. do Amaral, Jorge do Amaral, Ricardo Tanscheit, Marco A. C. Pacheco and Marley M. B. R. Vellasco); Chapter 4: Improving the Search by Encoding Multiple Solutions in a Chromosome (Mihai Oltean); Chapter 5: Real-World Evolutionary Designs: Secure Evolvable Hardware for Public-Key Cryptosystems (Nadia Nedjah and Luiza de Macedo Mourelle); Chapter 6: Automated Discovery of Innovative Designs of Mechanical Components Using Evolutionary Multi-Objective Algorithms (Kalyanmoy Deb and Shamik Chaudhuri); Chapter 7: Toward Efficient Topological Synthesis of Dynamic Systems Using Genetic Programming (Jianjun Hu, Kisung Seo, Erik D. Goodman and Ronald Rosenberg); Chapter 8: The Role of Simulated Evolution in Bioinformatics (Andy Auyeung and Ajith Abraham); Index