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May 27, 2021

Essentials of Metaheuristics

Essentials of Metaheuristics

Key Notes

  • Stochastic optimization -  employ some degree of randomness to find optimal solutions
  • Metaheuristics are applied to I know it when I see it problems
  • Hill-climbing is a simple metaheuristic algorithm
  • All metaheuristics are essentially elaborate combinations of hill-climbing and the random search


  • To Tweak a vector we might (as one of many possibilities) add a small amount of random noise to each number
  • Single-State Global Optimization Algorithms

Simulated Annealing

  • Simulated Annealing gets its name from annealing, a process of cooling molten metal
  • If R is better than S, we’ll always replace S with R as usual. But if R is worse than S, we may still replace S with R with a certain probability P(t, R, S):

Iterated Local Search

  • Iterated Local Search (ILS) tries to search through this space of local optima in a more intelligent fashion: it tries to stochastically hill-climb in the space of local optima

The Genetic Algorithm

To breed, we begin with an empty population of children. We then select two parents from the original population, copy them, cross them over with one another, and mutate the results.

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