This textbook introduces readers to the recent advances in the emerging field of genetic design automation (GDA). Starting with an introduction and the basic concepts of molecular biology, the authors provide an overview of various genetic design automation tools. The authors then present the DVASim tool (Dynamic Virtual Analyzer and Simulator) which is used for the analysis and verification of genetic logic circuits. This includes methods and algorithms for the timing and threshold value analyses of genetic logic circuits. Next, the book presents the GeneTech tool (A technology mapping tool for genetic circuits) and the methods developed for optimization, synthesis, and technology mapping of genetic circuits. Chapters are followed by exercises which give readers hands-on practice with the tools presented. The concepts and algorithms are thoroughly described, enabling readers to improve the tools or use them as a starting point to develop new tools. Both DVASim and GeneTech are available from the developer’s website, free of charge. This book is intended for a multidisciplinary audience of computer scientists, engineers and biologists. It provides enough background knowledge for computer scientists and engineers, who usually do not have any background in biology but are interested to get involved in this domain. This book not only presents an accessible basic introduction to molecular biology, it also includes software tools which allow users to perform laboratory experiments in a virtual in-silico environment. This helps newbies to get a quick start in understanding and developing genetic design automation tools. The third part of this book is particular useful for biologists who usually find it difficult to grasp programming and are reluctant to developing computer software. They are introduced to the graphical programming language, LabVIEW, from which they can start developing computer programs rapidly. Readers are further provided with small projects which will help them to start developing GDA tools.

Genetic algorithm Synthesis (GaS) is presented in this thesis. GaS is based on a hardware implementation of a genetic algorithm (GA), aimed at evolving the logic circuit of a defined input function, while minimizing the total number of resources utilized on the underlying hardware platform. A GA bases its operation on society itself, attempting to imitate natural selection in computing systems [1]. It has been found that GAs are very good search techniques, to be used when either the search space is vast or the present deterministic techniques are too restrictive. The field of logic synthesis, as well as technology mapping onto a field-programmable gate array (FPGA), contain both of the aforementioned obstacles, and thus a new method must be realized to overcome these obstacles. GaS is the integration of a multitude of technologies, and the realization of a solution for the automated synthesis of combinational logic circuits. The work falls under the evolvable hardware (EHW) [2] domain which is a very novel field of research. The presented system is fully functional, and has been prototyped onto a computing platform, which embeds itself into a personal computer, with the main communications path being the local bus. The front-end of GaS consists of a command-line interface, whence the user inputs both the truth table representation of the Boolean function to be evolved, as well as a few initializing parameters. The system then proceeds to evolve a solution for the user's problem. Experimental results are presented in this thesis. In simple terms, the speedup factors are very promising, and in future versions of the system, the improvements that could be realized will usher in a new computing paradigm: evolvable computing.

This research applies the biologically inspired, artificial evolutionary processes of Genetic Algorithms and Genetic Programming to digital hardware circuit synthesis and minimization. In this new application, three approaches are taken to genetic hardware development. First, as a method for logic synthesis, Genetic Programming is applied to the building of logic functions. Experimental results have shown the logic equations from this technique produce better than 88% coverage of the given truth-tables, but the method cannot guarantee complete (100%) coverage. Secondly, to better achieve complete function coverage, an XOR Correction Circuit Algorithm used in conjunction with the Genetic Logic Synthesis was developed. With this algorithm, the genetic logic synthesis can reiteratively attempt coverage by formulating its own selective "correction" functions, for input combinations where complete truth table coverage has not previously been achieved. With this technique, complete function coverage was synthesized in all experiments conducted. The third application of the paradigm is to the minimization of Reed-Muller Equations. In this application, a Genetic Algorithm is implemented only in the search space of all "correct", functionally equivalent equations, with only the task of finding reductions. With this limited search space the solutions have absolute guaranteed function coverage, as well as a better defined focus for the genetic evolutionary process. In both the logic synthesis and minimization processes the genetic operators determine efficient circuit implementations and reductions. The results are often different from those of human designers. Because the genetic techniques incorporate logical testing into the design and build process, one can be assured that the circuit will function as derived on completion. For all three applications, the effects of a number of evolutionary parameters on the genetic operators' problem solving capability are examined. The resulting logic and logic minimizations are also compared with both arbitrarily defined functions and well known logic synthesis benchmarks. It has been shown that genetic operators applied to digital logic can effectively find good solutions for both logic synthesis and logic minimization.

For the first time in book form, this comprehensive and systematic monograph presents methods for the reversible synthesis of logic functions and circuits. It is illustrated with a wealth of examples and figures that describe in detail the systematic methodologies of synthesis using reversible logic.

This book offers a basic introduction to genetic algorithms. It provides a detailed explanation of genetic algorithm concepts and examines numerous genetic algorithm optimization problems. In addition, the book presents implementation of optimization problems using C and C++ as well as simulated solutions for genetic algorithm problems using MATLAB 7.0. It also includes application case studies on genetic algorithms in emerging fields.

Logic Synthesis and Verification Algorithms is a textbook designed for courses on VLSI Logic Synthesis and Verification, Design Automation, CAD and advanced level discrete mathematics. It also serves as a basic reference work in design automation for both professionals and students. Logic Synthesis and Verification Algorithms is about the theoretical underpinnings of VLSI (Very Large Scale Integrated Circuits). It combines and integrates modern developments in logic synthesis and formal verification with the more traditional matter of Switching and Finite Automata Theory. The book also provides background material on Boolean algebra and discrete mathematics. A unique feature of this text is the large collection of solved problems. Throughout the text the algorithms covered are the subject of one or more problems based on the use of available synthesis programs.