Titlebook: Evolvable Systems: From Biology to Hardware; Third International Julian Miller,Adrian Thompson,Terence C. Fogarty Conference proceedings 2

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Initial Evaluation of an Evolvable Microwave Circuit,ure of distributed-constant circuits in the microwave range (i.e., over 1 GHz). These circuits are also difficult to adjust for optimum performance, even for experienced engineers. These related problems make development costs of microwave circuits very high. In order to overcome these problems, we

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Towards a Silicon Primordial Soup: A Fast Approach to Hardware Evolution with a VLSI Transistor Arr 16 transistors programmable in their channel dimensions as well as in their connectivity. A genetic algorithm is executed on a PC connected to one or more programmable transistor arrays (PTA). Individuals are represented by a given configuration of the PTA. The fitness of each individual is determi

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Understanding Inherent Qualities of Evolved Circuits: Evolutionary History as a Predictor of Fault ts designed by artificial evolution can exhibit very different inherent qualities to those designed by humans using conventional techniques. It is argued that some inherent qualities arising from the evolutionary approach can be beneficial if they are understood. As a case study, the paper seeks to

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,A Hardware Implementation of an Embryonic Architecture Using Virtex® FPGAs,teristic of the new cell is the structure of its memory. It is demonstrated that by implementing the memory as a look-up table, it is possible to synthesise an array of 25 cells in one XCV300 device. A frequency divider is presented as example of the application of embryonic arrays. After simulation

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Everything on the Chip: A Hardware-Based Self-Contained Spatially-Structured Genetic Algorithm for patially-structured evolutionary algorithm that provides significant speedup over conventional serial processing in three ways: (a) efficient hardware-pipelined fitness evaluation of individuals, (b) evaluation of an entire population of individuals in parallel, and (c) elimination of slow off-chip

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https://doi.org/10.1007/3-540-46406-9Hardware; artificial neural network; biology; electronics; evolution; neural network; robot; robotics