The autopolyploidy enhanced evolution of large-N fractal-random arrays

Joshua S. Petko, Douglas Henry Werner

Research output: Contribution to journalConference articlepeer-review


This paper introduces a novel approach for the optimization of large-N fractal random antenna arrays using genetic algorithms. Genetic algorithms often become overwhelmed by large numbers of input parameters, leading to an inefficient optimization processes. Fractal-random geometries lend themselves well to genetic algorithm optimization through the ability to describe their complex array structures with only a small number of input parameters. In addition, the recursive properties of fractal-random arrays allow for the rapid calculation of the array factor, which can be exploited to significantly speed up the convergence of the GA. This paper describes a method that increases the number of generators used to construct the array as the optimization progresses, maximizing the benefit of using fractal-random geometries to describe large-N arrays. Several optimized solutions are discussed, the largest being a 1650 element linear fractal-random array with a -23.57 dB side-lobe level and a beamwidth of 0.05°.

Original languageEnglish (US)
Article number1435959
Pages (from-to)922-926
Number of pages5
JournalIEEE National Radar Conference - Proceedings
Issue numberJanuary
StatePublished - Jan 1 2005
Event2005 IEEE International Radar Conference Record, RADAR 2005 - Arlington, United States
Duration: May 9 2005May 12 2005

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


Dive into the research topics of 'The autopolyploidy enhanced evolution of large-N fractal-random arrays'. Together they form a unique fingerprint.

Cite this