ISAMP:
for i = 1 to max_tries {
  try:
  set all variables to unassigned
    loop {
      if all variables are valued
      then return current assignment
      else {
        v = random unvalued variable
        assign v a randomly chosen value
        unit_propagate()
        if contradiction goto try
}}}
return failure

MaxWalkSat:
for i = 1 to max_tries {
  solution = random assignment
  for j = 1 to max_changes {
    if score(solution) > threshold
    then return solution
    c = random part of solution
    if p < random()
    then change a random setting in c
    else change setting in c that maximizes score(solution)
}}
return failure or best solution found

SA:
s = s0, e = energy(s)
sb = s, eb = e
k = 0
while k < kmax and e > emax {
  sn = neighbor(s)
  en = energy(sn)
  if en < eb
  then sb = sn, eb = en
  if acceptance(e, en, temperature(k/kmax)) > random()
  then s = sn, e = en
  k = k + 1
}
return sb

Beam:
while states is not empty {
  if first state is a goal state
  then return first state
  else {
    states = successors(first state) + other states
    sort states according to estimated cost to goal
    trim all but the best beam-width states
}}
return failure

A*:
while states is not empty {
  if first state is a goal state
  then return first state
  else {
    states = successors(first state) + other states
    sort states according to cost so far + estimated cost to goal
}}
return failure

KEYS:
for i = 1 to num_mitigations {
  for j = 1 to num_repeats {
    selected = settings from previous eras
    guessed = randomly selected values for other mitigations
    input = selected + guessed
    compute score(input)
  }
  divide scores into 10% best and 90% rest
  score mitigation values using BORE
  add the top-ranked mitigation value to selected
}
return selected