def genetic_search(problem, fitness_fn, ngen=1000, pmut=0.1, n=20):
    """Call genetic_algorithm on the appropriate parts of a problem.
    This requires the problem to have states that can mate and mutate,
    plus a value method that scores states."""
    s = problem.initial_state
    states = [problem.result(s, a) for a in problem.actions(s)]
    random.shuffle(states)
    return genetic_algorithm(states[:n], problem.value, ngen, pmut)
def genetic_search(problem, fitness_fn, ngen=1000, pmut=0.1, n=20):
    """Call genetic_algorithm on the appropriate parts of a problem.
    This requires the problem to have states that can mate and mutate,
    plus a value method that scores states."""
    s = problem.initial_state
    states = [problem.result(s, a) for a in problem.actions(s)]
    random.shuffle(states)
    return genetic_algorithm(states[:n], problem.value, ngen, pmut)
def genetic_search ( problem , fitness_fn , ngen = 1000 , pmut = 0.1 , n = 20 ) : s = problem . initial_state states = [ problem . result ( s , a ) for a in problem . actions ( s ) ] random . shuffle ( states ) return genetic_algorithm ( states [ : n ] , problem . value , ngen , pmut )
Call genetic_algorithm on the appropriate parts of a problem.
    This requires the problem to have states that can mate and mutate,
    plus a value method that scores states.
Call genetic_algorithm on the appropriate parts of a problem . This requires the problem to have states that can mate and mutate plus a value method that scores states .