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Ilja Bekman edited this page Oct 4, 2016 · 2 revisions

Example of genetic algorithm:

  • 11 Genes, 10 floating point range + 1 tuple
  • 100 individuals, max. 500 generations
  • adoptive Reproduction: take individuals making 50% of the generation fitness
  • Mutate random 50 by 3 genes each
  • Crossover all genes across all individuals
  • Evaluate, Sort

Fitness of the Fittest per Generation Mean Fitness per Generation

  • Ideal Genome with gaussian Mean, Sigma:
  1. 25.,5.
  2. 5.,1.
  3. "myBeryllium"
  4. 4.,0.5
  5. 80.,10.
  6. 14043.,3.
  7. 225.,30.
  8. 225.,30.
  9. 200.,15.
  10. 300.,100.
  11. 5.,1.
  • Recovered Genome after 234 generations
  1. 25.4868131832
  2. 4.97817102581
  3. myBeryllium
  4. 3.9851669444
  5. 79.8413385755
  6. 14043.1807813
  7. 224.218046061
  8. 225.219166708
  9. 203.013448816
  10. 296.675962235
  11. 5.04446313393
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