Random optimization (RO) refers to a family of numerical optimization methods that do not require the gradient of the problem to be optimized and RO can hence be used on functions that are not continuous or differentiable.
Their major disadvantage is that they are relatively slow, compared to other methods, such as random optimization. Recent speed improvements have focused on speciation, wherein cross-over can only occur if individuals are closely-enough related.
 See also: Simulated annealing, Genetic algorithm, Random search, Local search, Hill climbing
  
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