This is a lightweight framework that simplifies the process of creating genetic algorithms and running them in parallel.
What's your problem???
Let's say you've got a simple equality
a + 2b + 3c + 4d + 5e = 42 that you'd like come up with a solution for.
Create a Module
Start by creating a file and a module for your ga. Your module will be loaded into the framework and things inside it will be used to run your algroithm.
module equalityga # implement the GA interface inside here end
Define an Entity
Your entity should inherit from the abstract
GeneticAlgorithms.Entity. The framework will look for a
create_entity function and will use it to create an initial population.
type EqualityMonster <: Entity abcde::Array fitness EqualityMonster() = new(Array(Int, 5), nothing) EqualityMonster(abcde) = new(abcde, nothing) end function create_entity(num) # for simplicity sake, let's limit the values for abcde to be integers in [-42, 42] EqualityMonster(rand(Int, 5) % 43) end
EqualityMonster has a field
fitness. By default this field will be used by the framework to store the entities calculated fitness, so that you have access to it elsewhere in your GA. If you'd like to change the behavior you can overload
Create a Fitness Function
The framework will expect a
fitness function. It should take in a single entity and return a fitness score.
function fitness(ent) # we want the expression `a + 2b + 3c + 4d + 5e - 42` # to be as close to 0 as possible score = ent.abcde + 2 * ent.abcde + 3 * ent.abcde + 4 * ent.abcde + 5 * ent.abcde abs(score - 42) end
isless(l::Entity, r::Entity) will return
l.fitness < r.fitness, but that in this case entities with scores closer to 0 are doing better. So we should define a specialized
function isless(lhs::EqualityMonster, rhs::EqualityMonster) abs(lhs.fitness) > abs(rhs.fitness) end
group_entities operates on a population (an array of entities sorted by score) and will be run as a task and expected to emit groups of entities that will be passed into a crossover function.
group_entitites also provides a nice way to terminate the GA; if you want to stop, simply produce no groups.
function group_entities(pop) if pop.fitness == 0 return end # simple naive groupings that pair the best entitiy with every other for i in 1:length(pop) produce([1, i]) end end
crossover should take a group of parents and produce a new child entity. In our case we'll just grab properties from random parents.
function crossover(group) child = EqualityMonster() # grab each element from a random parent num_parents = length(group) for i in 1:length(group.abcde) parent = (rand(Uint) % num_parents) + 1 child.abcde[i] = group[parent].abcde[i] end child end
mutate operates on a single entity and is responsible for deciding whether or not to actually mutate.
function mutate(ent) # let's go crazy and mutate 20% of the time rand(Float64) < 0.8 && return rand_element = rand(Uint) % 5 + 1 ent.abcde[rand_element] = rand(Int) % 43 end
Run your GA!
using GeneticAlgorithms require("GeneticAlgorithms/test/equalityga.jl") model = runga(equalityga; initial_pop_size = 16) population(model) # the the latest population when the GA exited