Hostname: page-component-76fb5796d-9pm4c Total loading time: 0 Render date: 2024-04-25T16:58:31.839Z Has data issue: false hasContentIssue false
  • English
  • Français

Human-competitive evolved antennas

Published online by Cambridge University Press:  12 June 2008

Jason D. Lohn ,
Gregory S. Hornby  and
Derek S. Linden
Jason D. Lohn
Affiliation:
Carnegie Mellon University, NASA Ames Research Center, Moffett Field, California, USA
Gregory S. Hornby
Affiliation:
University of California Santa Cruz, NASA Ames Research Center, Santa Cruz, California, USA
Derek S. Linden
Affiliation:
JEM Engineering, Laurel, Maryland, USA
Get access Rights & Permissions [Opens in a new window]

Abstract

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.

Keywords

AntennaComputational DesignDesignEvolutionary ComputationGenetic ProgrammingSpacecraftWire Antenna
Type
Research Article
Information
AI EDAM , Volume 22 , Issue 3 , August 2008 , pp. 235 - 247
Copyright
Copyright © Cambridge University Press 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Adewuya, A. (1996). New methods in genetic search with real-valued chromosomes. MS Thesis. MIT, Mechanical Engineering Department.Google Scholar
Altshuler, E.E. (2002). Electrically small self-resonant wire antennas optimized using a genetic algorithm. IEEE Transactions on Antennas and Propagation 50, 297–300.CrossRefGoogle Scholar
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), 1–4.CrossRefGoogle Scholar
Altshuler, E.E., & Linden, D. (1997 b). Wire antenna designs using a genetic algorithm. IEEE Antenna & Propagation Society Magazine 39(1), 33–43.CrossRefGoogle Scholar
Burke, G. J., & Poggio, A. J. (1981). Numerical Electromagnetics Code (nec) Method of Moments. Technical Report UCID18834, Lawrence Livermore Lab.Google Scholar
Haupt, R.L. (1995). An introduction to genetic algorithms for electromagnetics. IEEE Antennas and Propagation Magazine 37, 7–15.CrossRefGoogle Scholar
Haupt, R.L. (1996). Genetic algorithm design of antenna arrays. IEEE Aerospace Applications Conf., Vol. 1., pp. 103–109.Google Scholar
Hornby, G.S., & Pollack, J.B. (2002). Creating high-level components with a generative representation for body-brain evolution. Artificial Life 8(3), 223–246.CrossRefGoogle ScholarPubMed
Linden, D.S. (1997). Automated design and optimization of wire antennas using genetic algorithms. PhD Thesis. MIT.Google Scholar
Linden, D.S. (2000). Wire antennas optimized in the presence of satellite structures using genetic algorithms. IEEE Aerospace Conf.Google Scholar
Linden, D.S., & Altshuler, E.E. (1996). Automating wire antenna design using genetic algorithms. Microwave Journal 39(3), 74–86.Google Scholar
Linden, D.S., & MacMillan, R. (2000). Increasing genetic algorithm efficiency for wire antenna design using clustering. ACES Special Journal on Genetic Algorithms.Google Scholar
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. 814–817.CrossRefGoogle Scholar
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), 1024–1031.CrossRefGoogle Scholar
Rahmat-Samii, Y., & Michielssen, E. (Eds.). (1999). Electromagnetic Optimization by Genetic Algorithms. New York: Wiley.Google Scholar