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.

From the explosion of interest, research, and applications of evolutionary computation a new field emerges-evolutionary electronics. Focused on applying evolutionary computation concepts and techniques to the domain of electronics, many researchers now see it as holding the greatest potential for overcoming the drawbacks of conventional design techniques. Evolutionary Electronics: Automatic Design of Electronic Circuits and Systems by Genetic Algorithms formally introduces and defines this area of research, presents its main challenges in electronic design, and explores emerging technologies. It describes the evolutionary computation paradigm and its primary algorithms, and explores topics of current interest, such as multi-objective optimization. The authors examine numerous evolutionary electronics applications, draw conclusions about those applications, and sketch the future of evolutionary computation and its applications in electronics. In coming years, the appearance of more and more advanced technologies will increase the complexity of optimization and synthesis problems, and evolutionary electronics will almost certainly become a key to solving those problems. Evolutionary Electronics is your key to discovering and unlocking the potential of this promising new field.

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.

Evolutionary Algorithms for Embedded System Design describes how Evolutionary Algorithm (EA) concepts can be applied to circuit and system design - an area where time-to-market demands are critical. EAs create an interesting alternative to other approaches since they can be scaled with the problem size and can be easily run on parallel computer systems. This book presents several successful EA techniques and shows how they can be applied at different levels of the design process. Starting on a high-level abstraction, where software components are dominant, several optimization steps are demonstrated, including DSP code optimization and test generation. Throughout the book, EAs are tested on real-world applications and on large problem instances. For each application the main criteria for the successful application in the corresponding domain are discussed. In addition, contributions from leading international researchers provide the reader with a variety of perspectives, including a special focus on the combination of EAs with problem specific heuristics. Evolutionary Algorithms for Embedded System Design is an excellent reference for both practitioners working in the area of circuit and system design and for researchers in the field of evolutionary concepts.

Evolvable hardware (EHW) refers to hardware whose architecture/structure and functions change dynamically and autonomously in order to improve its performance in carrying out tasks. The only single resource presenting both the fundamentals, and the latest advances in the field, this book teaches the basics of reconfigurable devices, why they are necessary and how they are designed.

Evolution through natural selection has been going on for a very long time. Evolution through artificial selection has been practiced by humans for a large part of our history, in the breeding of plants and livestock. Artificial evolution, where we evolve an artifact through artificial selection, has been around since electronic computers became common: about 30 years. Right from the beginning, people have suggested using artificial evolution to design electronics automatically.l Only recently, though, have suitable re configurable silicon chips become available that make it easy for artificial evolution to work with a real, physical, electronic medium: before them, ex periments had to be done entirely in software simulations. Early research concentrated on the potential applications opened-up by the raw speed ad vantage of dedicated digital hardware over software simulation on a general purpose computer. This book is an attempt to show that there is more to it than that. In fact, a radically new viewpoint is possible, with fascinating consequences. This book was written as a doctoral thesis, submitted in September 1996. As such, it was a rather daring exercise in ruthless brevity. Believing that the contribution I had to make was essentially a simple one, I resisted being drawn into peripheral discussions. In the places where I deliberately drop a subject, this implies neither that it's not interesting, nor that it's not relevant: just that it's not a crucial part of the tale I want to tell here.

The idea of evolving machines, whose origins can be traced to the cybernetics movementofthe1940sand1950s,hasrecentlyresurgedintheformofthenascent ?eld of bio-inspired systems and evolvable hardware. The inaugural workshop, Towards Evolvable Hardware, took place in Lausanne in October 1995, followed by the First International Conference on Evolvable Systems: From Biology to Hardware (ICES), held in Tsukuba, Japan in October 1996. The second ICES conference was held in Lausanne in September 1998, with the third and fourth being held in Edinburgh, April 2000 and Tokyo, October 2001 respectively. This has become the leading conference in the ?eld of evolvable systems and the 2003 conference promised to be at least as good as, if not better than, the four that preceeded it. The ?fth international conference was built on the success of its predec- sors, aiming at presenting the latest developments in the ?eld. In addition, it brought together researchers who use biologically inspired concepts to imp- ment real systems in arti?cial intelligence, arti?cial life, robotics, VLSI design and related domains. We would say that this ?fth conference followed on from the previous four in that it consisted of a number of high-quality interesting thought-provoking papers.