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Distributed Genetic Algorithm for feature selection in Gaia RVS spectra. Application to ANN parameterization. D.Fustes , D.Ordóñez , C.Dafonte , M.Manteiga and B. Arcay. Introduction.

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distributed genetic algorithm for feature selection in gaia rvs spectra

DistributedGeneticAlgorithmforfeatureselection in Gaia RVS spectra

Applicationto ANN parameterization

D.Fustes, D.Ordóñez, C.Dafonte, M.Manteiga and B. Arcay

introduction
Introduction
  • GGG (Galician Group for Gaia): Part of CU8 in DPAC. Involved in classification and parameterization tasks using AI techniques
  • Work with simulated data of the RVS instrument:
    • Estimation of physical parameters:
      • Effective temperatures
      • Superficial gravities
      • Metallicities
      • Abundancies of alpha elements
gaia rvs simulated data
Gaia RVS simulated data
  • Library compiled by A. Recio, P. de Laverny and B. Plez
  • 971 points per spectra.
  • Different SNR levels: 5,10,50, 200, ..
  • 70% data to train the Network and 30% to test the model
  • Use of ANN networks to perform the parameterization
discrete wavelet transform
Discrete Wavelet Transform
  • Redundant filtering process:
    • High-pass filters to generate Details
    • Low-pass filters to generate Approximations
  • Use of level 3 DWT: A3+D3+D2+D1, 997 points
feature selection
Feature selection
  • Reduce the spectra to fewer dimensionality
    • Reduce the complexity of the models
    • Reduce the computational needs
  • Variability-based methods: Reduce the dimensionality of a set capturing most of its variability (PCA)
    • They can not be specialized to capture the features relevant to the estimation of each parameter
  • Genetic Algorithm to select relevant areas for each parameter
genetic algorithm
Genetic algorithm
  • Based on the Evolution’s Theory
  • Best individuals reproduce and pass to the next generation
  • Fitness function: Train the ANN, test it and inverse the mean error. Computationally expensive!!!
distributed computation
Distributed computation
  • Huge computation needs lead to scalable solutions
  • Multicomputers are cheaper than supercomputers
  • Ways to distribute the algorithm
    • Low level: Distribute the ANN computation:
      • It should be performed in hardware
    • Medium level: Distribute the ANN learning
      • Possible with batch learning
      • Online learning perform better in this case
    • High level: Distribute the fitness computation
      • It was implemented in C++ with MPI and OpenMp
results 1
Results(1)
  • SNR 200
  • Original spectra
results 2
Results(2)
  • SNR 200
  • Wavelet domain