Population of individuals
Mutation – local search
Cross over – population holds information
Generations – iterations of improvement
GA Skeleton
P0 = initial population of size K
repeat until converged
compute fitness of all exP+
select “most fit” individuals (top half, weighted prab)
pair up individuals, replacing “least fit” individuals
vra crossover/mutiation
Randomized Optimization: mimic
– only points, no structure
– unclear probability distribution
minic: estimating distinations
p(x) = p(x1|x2…xn)p(x2|x3…xn)…p(xn)
x=[x1, x2, x3…xn]
