selection.py

import population
import random


def find_best(list1):
    best_fintness_value = max(list1)
    best_fintness_index = list1.index(best_fintness_value)
    return best_fintness_index, best_fintness_value


def find_worst(list1):
    worst_fintness_value = min(list1)
    worst_fintness_index = list1.index(worst_fintness_value)
    return worst_fintness_index, worst_fintness_value


def selection(pop):
    select_i = 0
    newfit_value = []
    total_fit = sum(pop.fitness)
    for i in range(len(pop.fitness)):
        if i == 0:
            newfit_value.append(pop.fitness[i] / total_fit)
        else:
            newfit_value.append(pop.fitness[i] / total_fit + newfit_value[i - 1])

    a = random.random()
    for i in range(len(pop.fitness)):
        if a < newfit_value[i]:
            select_i = i
            break
    return select_i