Contents


  • Introduction
  • 1. How evolutionary algorithms first appeared
  • 2. Evolutionary algorithms (methods)
  • 3. Genetic algorithms (GA)
    • 3.1. Field of application.
    • 3.2. Problems being solved
    • 3.3. Classic GA
    • 3.4. Search strategies
    • 3.5. Difference from the classic search of optimum
    • 3.6. Terminology of GA

  • 4. Advantages of GA
  • 5. Disadvantages of GA
  • 6. Experimental part
    • 6.1. Search for the best combination of predictors
      • with tabuSearch

    • 6.2. Search for the best parameters of TS:
      • with rgenoud (Genetic Optimization Using Derivatives)
      • with SOMA (Self-Organising Migrating Algorithm)
      • with GenSA (Generalized Simulated Annealing)


  • 7. Ways and methods of improving qualitative characteristics
  • Conclusion



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