# Code from Chapter 10 of Machine Learning: An Algorithmic Perspective (2nd Edition)
# by Stephen Marsland (http://stephenmonika.net)
# You are free to use, change, or redistribute the code in any way you wish for
# non-commercial purposes, but please maintain the name of the original author.
# This code comes with no warranty of any kind.
# Stephen Marsland, 2008, 2014
# The four peaks fitness function
import numpy as np
def fourpeaks(population):
T = 15
start = np.zeros((np.shape(population)[0],1))
finish = np.zeros((np.shape(population)[0],1))
fitness = np.zeros((np.shape(population)[0],1))
for i in range(np.shape(population)[0]):
s = np.where(population[i,:]==1)
f = np.where(population[i,:]==0)
if np.size(s)>0:
start = s[0][0]
else:
start = 0
if np.size(f)>0:
finish = np.shape(population)[1] - f[-1][-1] -1
else:
finish = 0
if start>T and finish>T:
fitness[i] = np.maximum(start,finish)+100
else:
fitness[i] = np.maximum(start,finish)
fitness = np.squeeze(fitness)
return fitness