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Human-competitive evolved antennas

  • Jason D. Lohn (a1), Gregory S. Hornby (a2) and Derek S. Linden (a3)


We present a case study showing a human-competitive design of an evolved antenna that was deployed on a NASA spacecraft in 2006. We were fortunate to develop our antennas in parallel with another group using traditional design methodologies. This allowed us to demonstrate that our techniques were human-competitive because our automatically designed antenna could be directly compared to a human-designed antenna. The antennas described below were evolved to meet a challenging set of mission requirements, most notably the combination of wide beamwidth for a circularly polarized wave and wide bandwidth. Two evolutionary algorithms were used in the development process: one used a genetic algorithm style representation that did not allow branching in the antenna arms; the second used a genetic programming style tree-structured representation that allowed branching in the antenna arms. The highest performance antennas from both algorithms were fabricated and tested, and both yielded very similar performance. Both antennas were comparable in performance to a hand-designed antenna produced by the antenna contractor for the mission, and so we consider them examples of human-competitive performance by evolutionary algorithms. Our design was approved for flight, and three copies of it were successfully flown on NASA's Space Technology 5 mission between March 22 and June 30, 2006. These evolved antennas represent the first evolved hardware in space and the first evolved antennas to be deployed.



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Adewuya, A. (1996). New methods in genetic search with real-valued chromosomes. MS Thesis. MIT, Mechanical Engineering Department.
Altshuler, E.E. (2002). Electrically small self-resonant wire antennas optimized using a genetic algorithm. IEEE Transactions on Antennas and Propagation 50, 297300.
Altshuler, E.E., & Linden, D.S. (1997 a). Design of a loaded monopole having hemispherical coverage using a genetic algorithm. IEEE Transactions on Antennas and Propagation 45(1), 14.
Altshuler, E.E., & Linden, D. (1997 b). Wire antenna designs using a genetic algorithm. IEEE Antenna & Propagation Society Magazine 39(1), 3343.
Burke, G. J., & Poggio, A. J. (1981). Numerical Electromagnetics Code (nec) Method of Moments. Technical Report UCID18834, Lawrence Livermore Lab.
Haupt, R.L. (1995). An introduction to genetic algorithms for electromagnetics. IEEE Antennas and Propagation Magazine 37, 715.
Haupt, R.L. (1996). Genetic algorithm design of antenna arrays. IEEE Aerospace Applications Conf., Vol. 1., pp. 103109.
Hornby, G.S., & Pollack, J.B. (2002). Creating high-level components with a generative representation for body-brain evolution. Artificial Life 8(3), 223246.
Linden, D.S. (1997). Automated design and optimization of wire antennas using genetic algorithms. PhD Thesis. MIT.
Linden, D.S. (2000). Wire antennas optimized in the presence of satellite structures using genetic algorithms. IEEE Aerospace Conf.
Linden, D.S., & Altshuler, E.E. (1996). Automating wire antenna design using genetic algorithms. Microwave Journal 39(3), 7486.
Linden, D.S., & MacMillan, R. (2000). Increasing genetic algorithm efficiency for wire antenna design using clustering. ACES Special Journal on Genetic Algorithms.
Lohn, J.D., Kraus, W.F., & Linden, D.S. (2002). Evolutionary optimization of a quadrifilar helical antenna. IEEE Antenna & Propagation Society Meeting, Vol. 3, pp. 814817.
Michielssen, E., Sajer, J.-M., Ranjithan, S., & Mittra, R. (1993). Design of lightweight, broad-band microwave absorbers using genetic algorithms. IEEE Transactions on Microwave Theory & Techniques 41(6), 10241031.
Rahmat-Samii, Y., & Michielssen, E. (Eds.). (1999). Electromagnetic Optimization by Genetic Algorithms. New York: Wiley.


Human-competitive evolved antennas

  • Jason D. Lohn (a1), Gregory S. Hornby (a2) and Derek S. Linden (a3)


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