36th DPS Meeting, 8-12 November 2004
Session 14 Future Missions
Poster I, Tuesday, November 9, 2004, 4:00-7:00pm, Exhibition Hall 1A

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[14.11] Evolutionary Computation Techniques for the Automated Design of Space Systems

R. J. Terrile, C. Adami, S. N. Chau, M. I. Ferguson, W. Fink, T. L. Huntsberger, G. Klimeck, M. A. Kordon, P. A. von Allmen (JPL)

The Evolvable Computation Group, at JPL, is tasked with demonstrating the utility of evolvable computation for computer-optimized automated design of 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.

Complex design problems are multi-parameter optimizations where physics models predict the outcome derived from a series of input parameters. Design, however, depends on desiring an outcome and deriving the necessary input parameters. Generally it is not feasible to invert the physics models to derive an optimal solution. However, by parallelizing the problem into a large population with varying input parameters and competing the results we can extract beneficial combinations of inputs. In the same way biological evolution functions, this process is repeated over many generations and uses the sophisticated biological operators of selection, mutation, and recombination to explore larger volumes of design space than could be examined by a human designer or by computational brute force.

Computationally derived evolutionary designs have shown competitive advantages over human created designs in complexity, creativity and robustness. Our group has demonstrated this in the areas of power system design, low thrust trajectory optimization, robotic arm path finding, MEMS micro-gyro calibration, mission planning and scheduling and in avionics architecture design. We have also developed a framework for the rapid introduction and parallelization of optimization problems in an evolutionary environment using computer clusters.

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Bulletin of the American Astronomical Society, 36 #4
© 2004. The American Astronomical Soceity.