Let's see how we can visualize the evolution process. In DEAP, they have used a method called Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to visualize the evolution. It is an evolutionary algorithm that's used to solve non-linear problems in the continuous domain. CMA-ES technique is robust, well studied, and is considered as state of the art in evolutionary algorithms. Let's see how it works by delving into the code provided in their source code. The following code is a slight variation of the example shown in the DEAP library.

Create a new Python file and import the following:

import numpy as np 
import matplotlib.pyplot as plt 
from deap import algorithms, base, benchmarks, \ 
        cma, creator, tools 

Define a function to create the toolbox. We will define a FitnessMin function using negative weights:

# Function to create a toolbox 
def create_toolbox(strategy): 
    creator.create("FitnessMin", base.Fitness, weights...