Hands-On Genetic Algorithms with Python

Optimizing Himmelblau's function with genetic algorithms

The genetic algorithm-based program we created for finding a single minimum of Himmelblau's function resides in the Python 02-optimize-himmelblau.py program, located at:

https://github.com/PacktPublishing/Hands-On-Genetic-Algorithms-with-Python/blob/master/Chapter06/02-optimize-himmelblau.py

The program is similar to the one we used for optimizing the Eggholder function, with a few differences highlighted as follows:

1. We set the boundaries for this function to [-5.0, 5.0]:

BOUND_LOW, BOUND_UP = -5.0, 5.0  # boundaries for all dimensions

2. We now use Himmelblau's function as the fitness evaluator:

def himmelblau(individual):    x = individual[0]    y = individual[1]    f = (x ** 2 + y - 11) ** ...