Evolving Antennas for Ultra-High Energy Neutrino Detection

  title={Evolving Antennas for Ultra-High Energy Neutrino Detection},
  author={Julie Rolla and A. Connolly and Kai Staats and Stephanie Wissel and Dean Y. Arakaki and Ian Best and Adam Blenk and B. A. Clark and Maximillian Clowdus and Suren Gourapura and Corey Harris and Hannah Hasan and Luke Letwin and David W. Liu and Carl Pfendner and Jordan Potter and Cade Sbrocco and Tom Sinha and Jacob Trevithick},
Evolutionary algorithms borrow from biology the concepts of mutation and selection in order to evolve optimized solutions to known problems. The GENETIS collaboration is developing genetic algorithms for designing antennas that are more sensitive to ultra-high energy neutrino- induced radio pulses than current designs. There are three aspects of this investigation. The first is to evolve simple wire antennas to test the concept and different algorithms. Second, optimized antenna response… 


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An Adaptive Genetic Algorithm Based on Population Diversity Strategy
  • Chen Lin
  • Computer Science
    2009 Third International Conference on Genetic and Evolutionary Computing
  • 2009
An adaptive genetic algorithm which adjusts probability of mutation dynamicly based on average square deviation of population fitness value that shows the population diversity to solve premature problem is presented.