Evolutionary Algorithms


WARNING: This background section is way too much fun. The SoftDes teaching team cannot be held responsible for lost productivity that may result. You might want to grab a comfy chair and a frosty beverage before proceeding.

Evolutionary algorithms use techniques inspired by biological evolution to find approximate solutions to complex optimization problems. In brief, these heuristic algorithms represent potential solutions as individuals within a population. New solutions are generated from the population via mutation and reproduction, and the fittest survive to form the next generation.

Example 1: Evolving biped gaits

Flexible Muscle-Based Locomotion for Bipedal Creatures

The gaits shown in these videos were not designed: they were evolved. The researchers provided an environment with constraints (physics) plus a “fitness function” for evaluating individuals (able to walk the farthest without falling), and “natural selection” did the rest without human intervention.

Notice how the same algorithm is able to flexibly adapt to different constraints (walking on uneven terrain or on the moon). It is also fascinating to watch it rediscover many efficient gaits found in nature.

Example 2: Evolving cars

  1. Go to http://boxcar2d.com.
  2. Don’t forget to close your browser at some point to eat and sleep.

This physics simulation attempts to evolve cars to successfully negotiate randomly generated terrain. Each car is represented by a simple “genotype” — a vector describing the polygons and wheels that comprise the car’s body. The BoxCar 2D algorithm description has more details about how new cars are generated and selected.

Example 3: Evolving FPGAs

Read: On the Origin of Circuits (1500 words)

This example brings evolutionary algorithms into the hardware world: a researcher was able to evolve a Field Programmable Gate Array (a type of reprogrammable logic chip) to distinguish between two audio tones. The resulting circuits were radically different from anything a human designer would ever come up with.

Get set

GitHub Classroom invite

You should see evolve_text.py within the toolbox folder.

For this exercise, we will be using the Distributed Evolutionary Algorithms in Python (DEAP) framework, as well as NumPy for collecting some statistics. Install them both by running:

$ pip install deap
$ pip install numpy

Note: The problem we will tackle in this exercise is simple enough that we could implement it from scratch with no framework, but DEAP is a powerful set of tools that you might find useful in the future.

Let’s talk about all the good things and the bad things that may be

In this toolbox exercise, you will implement a genetic algorithm (GA) to evolve an initial population of random strings into a specified string.

Caveat: Although it’s a good learning exercise, this problem is not a particularly appropriate application for evolutionary algorithms, since the exact solution and the shape of the search space are known in advance. For more, check out “When are Evolutionary Algorithms Useful?” and this list of Applications of Genetic Algorithms.

In general, genetic algorithms follow these basic steps:

  1. Create an “genotype” encoding of problem solutions
  2. Generate an initial population of individuals with random genotypes
  3. Using a problem-specific “fitness function”, select the best individuals from the population and kill off the rest
  4. Mate the selected individuals to create a new generation of individuals
  5. Randomly mutate the genotypes of some individuals
  6. Repeat 3-5 for as many generations as required

Implementing genetic operators

Let’s take a deeper look at each of these steps, and implement each in turn.

First, look through the provided starter code and try to identify the components of the genetic algorithm.

Genotype representation

The individuals in our population are represented as Message objects, each of which hold a text string.

Design decision: Why is this implemented with a list of characters instead of a string?

Evaluating fitness

The fitness function is usually the most problem-specific component of any GA. For this exercise, Messages that are closer to the target text are more fit. Unlike most problems GAs tackle, the optimal solution is known — when the Message is identical to the target text. To make this more precise, the goal for this GA is to minimize the Levenshtein distance between the Message and the target text.

Implement the levenshtein_distance function.

You will need the memoized version to get any reasonable performance.


Mating combines pieces of the genotypes of two parent Messages to produce offspring. For this exercise we will use a two point crossover technique, which has the following (pseudocode) behavior:

  >>> parent1 = "ABCDEF"
  >>> parent2 = "UVWXYZ"
  >>> print(TwoPointCrossover(parent1, parent2))

This crossover is intended to model homologous recombination of chromosomes that occurs during sexual reproduction.

Skills check: We are using the tools.cxTwoPoints function built into DEAP. Implement your own crossover function, and update the “mate” alias in get_toolbox to use your version.


Mutation modifies the genotype of a single individual, and is intended to model chromosome mutation. The types of mutation possible in this exercise are:

Implement the mutate_text function, with independent probabilities for each mutation type.

Evolving text

Once you’ve made the modifications in the previous section, it’s time to evolve some text! To call your program, run:

$ python evolve_text.py [goal_message]

where goal_message is an optional argument specifying the target text. Excerpts from a sample execution of the program are given below.

[Generation 0]
Message('JUPN') [Distance: 33]
[Generation 10]
Message('NDAEOS OIDEAN YWIZESU') [Distance: 25]
Message('RBA HAQSKYFWHYWPWNTFEBT') [Distance: 25]
[Generation 20]
Message('IUEBTS MKYBWH YWITETRS') [Distance: 17]
[Generation 100]
Message('IUMBATS MNKFYS WIH TYWSITERS')  [Distance: 11]
[Generation 200]
[Generation 500]


The performance of a genetic algorithm on a particular problem depends on the genetic operators used, as well as the parameters selected. A few suggestions for things to try:

Choose at least one of these experiments (or a different one of your own design) and study how it changes the genetic algorithm’s performance (e.g. number of generations to converge to the target text).

What to turn in

Push both your evolve_text.py and a results.txt with the findings from your experimentation to GitHub.

Going beyond

Further reading