Current techniques in evolutionary synthesis of analogue and digital circuits designed at transistor level have focused on achieving the desired functional response, without paying sufficient attention to issues needed for a practical implementation of the resulting solution. No silicon fabrication of circuits with topologies designed by evolution has been done before, leaving open questions on the feasibility of the evolutionary circuit design approach, as well as on how highperformance, robust, or portable such designs could be when implemented in hardware. It is argued that moving from evolutionary 'design-for experimentation' to 'design-for-implementation' requires, beyond inclusion in the fitness function of measures indicative of circuit evaluation factors such as power consumption and robustness to temperature variations, the addition of certain evaluation techniques that are not common in conventional design. Several such techniques that were found to be useful in evolving designs for implementation are presented; some are general, and some are particular to the problem domain of transistor-level logic design, used here as a target application. The example used here is a multifunction NAND/NOR logic gate circuit, for which evolution obtained a creative circuit topology more compact than what has been achieved by multiplexing a NAND and a NOR gate. The circuit was fabricated in a 0.5 mm CMOS technology and silicon tests showed good correspondence with the simulations.

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12 We propose a tuning method for MEMS gyroscopes based on evolutionary computation that has the capacity to efficiently increase the sensitivity of MEMS gyroscopes through tuning and, furthermore, to find the optimally tuned configuration for this state of increased sensitivity. The tuning method was tested for the second generation JPL/Boeing Post-resonator MEMS gyroscope using the measurement of the frequency response of the MEMS device in open-loop operation.

Abstract-The Evolvable Computation Group, 1,2 at NASA's Jet Propulsion Laboratory, is tasked with demonstrating the utility of computational engineering and computer optimized design for complex space systems. The group is comprised of researchers over a broad range of disciplines including biology, genetics, robotics, physics, computer science and system design, and employs biologically inspired evolutionary computational techniques to design and optimize complex systems. Over the past two years we have developed tools using genetic algorithms, simulated annealing and other optimizers to improve on human design of space systems. We have further demonstrated that the same tools used for computeraided design and design evaluation can be used for automated innovation and design.These powerful techniques also serve to reduce redesign costs and schedules.

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