Evolvable 🦎

Gem Version Maintainability

An evolutionary framework for writing programs that use operations such as selection, crossover, and mutation. Explore ideas generatively in any domain, discover novel solutions to complex problems, and build intuitions about intelligence, complexity, and the natural world.

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Table of Contents


Add gem "evolvable" to your Gemfile and run bundle install or install it yourself with: gem install evolvable

Getting Started

The Evolvable module makes it possible to implement evolutionary behaviors for any class by defining a .search_space class method and #value instance method. Then to evolve instances, initialize a population with .new_population and invoke the #evolve method on the resulting population object.

Implementation Steps

  1. Include the Evolvable module in the class you want to evolve.
  2. Define .search_space and any gene classes that you reference.
  3. Define #value.
  4. Initialize a population with .new_population and use #evolve.

To demonstrate these steps, we'll look at the Hello World example program.

Hello World

Let's build the evolvable hello world program using the above steps. It'll evolve a population of arbitrary strings to be more like a given target string. After installing this gem, run evolvable hello at the command line to see it in action.

Below is example output from evolving a population of randomly initialized string objects to match "Hello World!", then "Hello Evolvable World".

❯ Enter a string to evolve: Hello World!

pp`W^jXG'_N`%              Generation 0
H-OQXZ\a~{H*               Generation 1 ...
HRv9X WorlNi               Generation 50 ...
HRl6W World#               Generation 100 ...
Hello World!               Generation 165

❯ Enter a string to evolve: Hello Evolvable World

Helgo World!b+=1}3         Generation 165
Helgo Worlv!}:c(SoV        Generation 166
Helgo WorlvsC`X(Joqs       Generation 167
Helgo WorlvsC`X(So1RE      Generation 168 ...
Hello Evolv#"l{ Wor*5      Generation 300 ...
Hello Evolvable World      Generation 388

Step 1

Let's begin by defining a HelloWorld class and have it include the Evolvable module.

class HelloWorld
  include Evolvable

Step 2

Now we can define the .search_space class method with the types of genes that we want our our evolvable "hello world" instances to be able to have. We'll use CharGene instances to represent single characters within strings. So an instance with the string value of "Hello" would be composed of five CharGene instances.

class HelloWorld
  include Evolvable

  def self.search_space
    ["CharGene", 1..40]

The Search Space can be defined in a variety of ways. The above is shorthand that's useful for when there's only one type of gene. This method can also return an array of arrays or hash.

The 1..40 specifies the range of possible genes for a particular HelloWorld instance. Evolvable translates this range or integer value into a Evolvable::CountGene object.

By specifying a range, an Evolvable::CountGene instance can change the number of genes that are present in an evovlable instance. Count genes undergo evolutionary operations like any other gene. Their effects can be seen in the letter changes from Generation 165 to 168 in the above example output.

To finish step 2, we'll define the gene class that we referenced in the above .search_space method. Gene classes should include the Evolvable::Gene module.

class CharGene
  include Evolvable::Gene

  def self.chars
    @chars ||= 32.upto(126).map(&:chr)

  def to_s
    @to_s ||= self.class.chars.sample

It's important that, once accessed, the data for a particular gene never change. When the #to_s method first runs, Ruby's ||= operator memoizes the result of randomly picking a char, enabling this method to sample a char only once per gene.

After defining the search space, we can now initialize HelloWorld instances with random genes, but to actually evolve them, we need to define the #value instance method. It provides the basis for comparing different evolvable instances.

Step 3

In the next step, we'll set the goal value to 0, so that evolution favors evolvable HelloWorld instances with #value methods that return numbers closer to 0. That means we want instances that more closely match their targets to return scores nearer to 0. As an example, if our target is "hello world", an instance that returns "jello world" would have a value of 1 and "hello world" would have a value of 0.

For a working implementation, see the #value method in examples/hello_world.rb

Step 4

Now it's time to initialize a population with .new_population. By default, evolvable populations seek to maximize numeric values. In this program, we always know the best possible value, so setting the goal to a concrete number makes sense. This is done by passing the evaluation params with equalize set to 0.

We'll also specify the number of instances in a population using the population's size parameter and change the mutation porbability from 0.03 (3%) to 0.6 (60%).

Experimentation has suggested that a large mutation probability tends to decrease the time it takes to evolve matches with short strings and has the opposite effect for long strings. This is demonstrated in the example output above by how many generations it took to go from "Hello World!" to "Hello Evolvable World". As an optimization, we could dynamically change the mutation probability using a population hook detailed below, but doing so will be left as an exercise for the reader. Pull requests are welcome.

population = HelloWorld.new_population(size: 100,
                                       evaluation: { equalize: 0 },
                                       mutation: { probability: 0.6 }

At this point, everything should work when we run population.evolve, but it'll look like nothing is happening. The next section will allow us to gain instight by hooking into the evolutionary process.

Evolvable Population Hooks

The following class methods can be implemented on your Evolvable class, e.g. HelloWorld, to hook into the Population#evolve lifecycle. This is useful for logging evolutionary progress, optimizing parameters, and creating interactions with and between evolvable instances.

  1. .before_evaluation(population) - Runs before evaluation.

  2. .before_evolution(population)- Runs after evaluation and before evolution.

  3. .after_evolution(population) - Runs after evolution.

Let's define .before_evolution to print the best value for each generation. We'll also define HelloWorld#to_s, which implicitly delegates to CharGene#to_s during the string interpolation that happens.

class HelloWorld
  include Evolvable

  def self.before_evolution(population)
    best_evolvable = population.best_evolvable
    evolutions_count = population.evolutions_count
    puts "#{best_evolvable} - Generation #{evolutions_count}"

  # ...

  def to_s
    @to_s ||= genes.join

  # ...

Finally we can evolve the population with the Evolvable::Population#evolve instance method.


You now know the fundamental steps to building evolvable programs of endless complexity in any domain! 🐸 The exact implementation for the command line demo can be found in exe/hello and examples/hello_world.rb.


Populations are composed of evolvables which are composed of genes. Evolvables orchestrate behaviors by delegating to gene objects. Collections of genes are organized into genomes and constitute the search space. Evaluation and evolution objects are used to evolve populations. By default, evolution is composed of selection, combination, and mutation.

The following concept map depicts how genes flow through populations.

Concept Map

Evolvable is designed with extensibility in mind. Evolvable objects such as evaluation, evolution, selection, combination, and mutation can be extended and swapped, potentially in ways that alter the above graph.


For evolution to be effective, an evolvable's genes must be able to influence its behavior. Evolvables are composed of genes that can be used to run simple functions or orchestrate complex interactions. The level of abstraction is up to you.

Defining gene classes requires encapsulating some "sample space" and returning a sample outcome when a gene attribute is accessed. For evolution to proceed in a non-random way, the same sample outcome should be returned every time a particular gene is accessed with a particular set of parameters. Memoization is a useful technique for doing just this. The memo_wise gem may be useful for more complex memoizations.

# This gene generates a random hexidecimal color code for use by evolvables.

require 'securerandom'

class ColorGene
  include Evolvable::Gene

  def hex_code
    @hex_code ||= SecureRandom.hex(3)



Population objects are responsible for generating and evolving instances. They orchestrate all the other Evolvable objects to do so.

Populations can be initialized and re-initialized with a number of useful parameters.

# TODO: initialize a population with all supported parameters



For selection to be effective in the context of evolution, there needs to be a way to compare evolvables. In the genetic algorithm, this is often referred to as the "fitness function".

The Evolvable::Evaluation object expects evolvable instances to define a #value method that returns some numeric value. Values are used to evaluate instances relative to each other and with regards to some goal. Out of the box, the goal can be set to maximize, minimize, or equalize numeric values.

# TODO: Show how to add/change population's evaluation object

# The goal value can also be assigned via as argument to `Evolvable::Population#evolve`
population.evolve(goal_value: 1000)



After a population's instances are evaluated, they undergo evolution. The default evolution object is composed of selection, crossover, and mutation objects and applies them as operations to a population's evolvables in that order.



The selection object assumes that a population's evolvables have already been sorted by the evaluation object. It selects "parent" evolvables to undergo combination and thereby produce the next generation of evolvables.

Only two evolvables are selected as parents for each generation by default. The selection size is configurable.

# TODO: Show how to add/change population's selection object



Combination generates new evolvable instances by combining the genes of selected instances. You can think of it as a mixing of parent genes from one generation to produce the next generation.

You may choose from a selection of combination objects or implement your own. The default combination object is Evolvable::GeneCombination.



Mutation serves the role of increasing genetic variation. When an evolvable undergoes a mutation, one or more of its genes are replaced by newly initialized ones. In effect, a gene mutation invokes a new random outcome from the genetic search space.

Mutation frequency is configurable using the probability and rate parameters.

# Show how to initialize/assign population with a specific mutation object


Search Space

The search space encapsulates the range of possible genes for a particular evolvable. You can think of it as the boundaries of genetic variation. It is configured via the .search_space method that you define on your evolvable class. It's used by populations to initialize new evolvables.

Evolvable provides flexibility in how you define your search space. The below example implementations for .search_space produce the exact same search space for the Hello World demo program. The different styles arguably vary in suitability for different contexts, perhaps depending on how programs are loaded and the number of different gene types.

# All 9 of these example definitions are equivalent

# Hash syntax
{ chars: { type: 'CharGene', max_count: 100 } }
{ chars: { type: 'CharGene', min_count: 1, max_count: 100 } }
{ chars: { type: 'CharGene', count: 1..100 } }

# Array of arrays syntax
[[:chars, 'CharGene', 1..100]]
[['chars', 'CharGene',  1..100]]
[['CharGene', 1..100]]

# A single array works when there's only one type of gene
['CharGene', 1..100]
[:chars, 'CharGene', 1..100]
['chars', 'CharGene', 1..100]



Bug reports and pull requests are welcome on GitHub at https://github.com/mattruzicka/evolvable.

If you're interested in contributing, but don't know where to get started, message me on twitter at @mattruzicka.