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Variation

🌐 public · created AE531128 (28.11.2023) · by DDH · open in the standard editor view · 📽 open as presentation

In evolutionary optimization, variation is the process of introducing diversity into the population of solutions. Like genetic mutations and breeding in nature, it involves altering the 'genes' (parameters) of candidate solutions to create new, different ones. This can be done through mutation (changing some parameters) or crossover (mixing parameters from two solutions). Variation is crucial for exploring new solutions and avoiding getting stuck with suboptimal ones, much like how biological diversity is key to the survival and evolution of species.

Ancestors (1 superordinated path)

root/ baumhaus.digital/ Design & Computation/ Perspectives in Engineering/ IOPS/ Heuristics/ Nature-inspired heuristics/ Evolutionary optimization/ Genetic Algorithm/ Variation

Descendants (at least 3 branches originate here)

  • Variation
    In evolutionary optimization, variation is the process of introducing diversity into the population of solutions. Like genetic mutations and breeding in nature,
    • is_parent Mutation ·
      for numeric genes: additive mutation, multiplicative mutation, complex (imaginary) mutation for symbolic ones: removal, addition or replacement of a symbol; met
    • is_parent Crossover ·
      Crossover
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