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W. Banzhaf

Publications and source records attributed to W. Banzhaf.

3 recordsLinked to original sources

Using Evolutionary Algorithms to Design Antennas with Greater Sensitivity to Ultra High Energy Neutrinos

The Genetically Evolved NEutrino Telescopes for Improved Sensitivity, or GENETIS, project seeks to optimize detectors in physics for science outcomes in high dimensional parameter spaces. In this project, we designed an antenna using a genetic algorithm with a science outcome directly as the sole figure of merit. This paper presents initial results on the improvement of an antenna design for in ice neutrino detectors using the current Askaryan Radio Array, or ARA, experiment as a baseline. By optimizing for the effective volume using the evolved antenna design in ARA, we improve upon ARAs simulated sensitivity to ultra high energy neutrinos by 22 percent, despite using limited parameters in this initial investigation. Future improvements will continue to increase the computational efficiency of the genetic algorithm and the complexity and fitness of the antenna designs. This work lays the foundation for continued research and development of methods to increase the sensitivity of detectors in physics and other fields in parameter spaces of high dimensionality.

astro-ph.IM

Faster Genetic Programming GPquick via multicore and Advanced Vector Extensions

We evolve floating point Sextic polynomial populations of genetic programming binary trees for up to a million generations. Programs with almost four hundred million instructions are created by crossover. To support unbounded Long-Term Evolution Experiment LTEE GP we use both SIMD parallel AVX 512 bit instructions and 48 threads to yield performance of up to 139 billion GP operations per second, 139 giga GPops, on a single Intel Xeon Gold 6126 2.60GHz server.

cs.NE

Evolving Genes to Balance a Pole

We discuss how to use a Genetic Regulatory Network as an evolutionary representation to solve a typical GP reinforcement problem, the pole balancing. The network is a modified version of an Artificial Regulatory Network proposed a few years ago, and the task could be solved only by finding a proper way of connecting inputs and outputs to the network. We show that the representation is able to generalize well over the problem domain, and discuss the performance of different models of this kind.

cs.AI