Generalized disjunction decomposition for evolvable hardware

Evolvable hardware (EHW) refers to self-reconfiguration hardware design, where the configuration is under the control of an evolutionary algorithm (EA). One of the main difficulties in using EHW to solve real-world problems is scalability, which limits the size of the circuit that may be evolved. This paper outlines a new type of decomposition strategy for EHW, the “generalized disjunction decomposition” (GDD), which allows the evolution of large circuits. The proposed method has been extensively tested, not only with multipliers and parity bit problems traditionally used in the EHW community, but also with logic circuits taken from the Microelectronics Center of North Carolina (MCNC) benchmark library and randomly generated circuits. In order to achieve statistically relevant results, each analyzed logic circuit has been evolved 100 times, and the average of these results is presented and compared with other EHW techniques. This approach is necessary because of the probabilistic nature of EA; the same logic circuit may not be solved in the same way if tested several times. The proposed method has been examined in an extrinsic EHW system using the$(1 + lambda)$evolution strategy. The results obtained demonstrate that GDD significantly improves the evolution of logic circuits in terms of the number of generations, reduces computational time as it is able to reduce the required time for a single iteration of the EA, and enables the evolution of larger circuits never before evolved. In addition to the proposed method, a short overview of EHW systems together with the most recent applications in electrical circuit design is provided

On the Evolutionary Design of Quantum Circuits

The goal of this work is to understand the application of the evolutionary programming approach to the problem of quantum circuit design. This problem is motivated by the following observations: In order to keep up with the seemingly insatiable demand for computing power our computing devices will continue to shrink, all the way down to the atomic scale, at which point they become quantum mechanical systems. In fact, this event, known as Moore?s Horizon, is likely to occur in less than 25 years. The recent discovery of several quantum algorithms which can solve some interesting problems more efficiently than any known classical algorithm. While we are not yet certain that quantum computers will ever be practical to build, there do now exist the first few astonishing experimental devices capable of briefly manipulating small quantities of quantum information. The programming of these devices is already a nontrivial problem, and as these devices and their algorithms become more complicated this problem will quickly become a significant challenge. The Evolutionary Programming (EP) approach to problem solving seeks to mimic the processes of evolutionary biology which have resulted in the awesome complexity of living systems, almost all of which are well beyond our current analysis and engineering capabilities. This approach is motivated by the highly successful application of Koza?s Genetic Programming (GP) approach to a variety of circuit design problems, and specifically the preliminary reports byWilliams and Gray and also Rubinstein who applied GP to quantum circuit design. Accompanying this work is software for evolutionary quantum circuit design which incorporates several advances over previous approaches, including: A formal language for describing parallel quantum circuits out of an arbitary elementary gate set, including gates with one or more parameters. A fitness assessment procedure that measures both average case fidelity with a respect for global phase equivalences, and implementation cost. A Memetic Programming (MP) based reproductive strategy that uses a combination of global genetic and local memetic searches to effectively search through diverse circuit topologies and optimize the parameterized gates they contain. Several benchmark experiments are performed on small problems which support the conclusion that Evolutionary Programming is a viable approach to quantum circuit design and that further experiments utilizing more computational resources and more problem insight can be expected to yield many new and interesting quantum circuits

Substructural Analysis Using Evolutionary Computing Techniques

Substructural analysis (SSA) was one of the very first machine learning techniques to be applied to chemoinformatics in the area of virtual screening. For this method, given a set of compounds typically defined by their fragment occurrence data (such as 2D fingerprints). The SSA computes weights for each of the fragments which outlines its contribution to the activity (or inactivity) of compounds containing that fragment. The overall probability of activity for a compound is then computed by summing up or combining the weights for the fragments present in the compound. A variety of weighting schemes based on specific relationship-bound equations are available for this purpose. This thesis identifies uplift to the effectiveness of SSA, using two evolutionary computation methods based on genetic traits, particularly the genetic algorithm (GA) and genetic programming (GP). Building on previous studies, it was possible to analyse and compare ten published SSA weighting schemes based on a simulated virtual screening experiment. The analysis showed the most effective weighting scheme to be the R4 equation which was a part of document-based weighting schemes. A second experiment was carried out to investigate the application of GA-based weighting scheme for the SSA in comparison to an experiment using the R4 weighting scheme. The GA algorithm is simple in concept focusing purely on suitable weight generation and effective in operation. The findings show that the GA-based SSA is superior to the R4-based SSA, both in terms of active compound retrieval rate and predictive performance. A third experiment investigated the genetic application via a GP-based SSA. Rigorous experiment results showed that the GP was found to be superior to the existing SSA weighting schemes. In general, however, the GP-based SSA was found to be less effective than the GA-based SSA. A final experimented is described in this thesis which sought to explore the feasibility of data fusion on both the GA and GP. It is a method producing a final ranking list from multiple sets of ranking lists, based on several fusion rules. The results indicate that data fusion is a good method to boost GA-and GP-based SSA searching. The RKP rule was considered the most effective fusion rule

Intrinsic Hardware Evolution on the Transistor Level

This thesis presents a novel approach to the automated synthesis of analog circuits. Evolutionary algorithms are used in conjunction with a fitness evaluation on a dedicated ASIC that serves as the analog substrate for the newly bred candidate solutions. The advantage of evaluating the candidate circuits directly in hardware is twofold. First, it may speed up the evolutionary algorithms, because hardware tests can usually be performed faster than simulations. Second, the evolved circuits are guaranteed to work on a real piece of silicon. The proposed approach is realized as a hardware evolution system consisting of an IBM compatible general purpose computer that hosts the evolutionary algorithm, an FPGA-based mixed signal test board, and the analog substrate. The latter one is designed as a Field Programmable Transistor Array (FPTA) whose programmable transistor cells can be almost freely connected. The transistor cells can be configured to adopt one out of 75 different channel geometries. The chip was produced in a 0.6µm CMOS process and provides ample means for the input and output of analog signals. The configuration is stored in SRAM cells embedded in the programmable transistor cells. The hardware evolution system is used for numerous evolution experiments targeted at a wide variety of different circuit functionalities. These comprise logic gates, Gaussian function circuits, D/A converters, low- and highpass filters, tone discriminators, and comparators. The experimental results are thoroughly analyzed and discussed with respect to related work