Running:

For now, just modify the script in paralleleureqa.jl to your liking and run:

julia --threads auto -O3 paralleleureqa.jl

Modification

You can change the binary and unary operators in eureqa.jl here:

const binops = [plus, mult]
const unaops = [sin, cos, exp];

E.g., you can add another binary function with:

const binops = [plus, mult, (x, y)->x^2*y]

You can change the dataset here:

const nvar = 5;
const X = rand(100, nvar);
# Here is the function we want to learn (x2^2 + cos(x3))
const y = ((cx,)->cx^2).(X[:, 2]) + cos.(X[:, 3])

by either loading in a dataset, or modifying the definition of y.

Hyperparameters

Turn on annealing by setting the following in paralleleureqa.jl:

const annealing = true

Annealing allows each evolutionary cycle to turn down the exploration rate over time: at the end (temperature 0), it will only select solutions better than existing solutions.

The following parameter, parsimony, is how much to punish complex solutions: const parsimony = 0.01

Finally, the following determins how much to scale temperature by (T between 0 and 1). const alpha = 10.0 Larger alpha means more exploration.

One can also adjust the relative probabilities of each mutation here:

weights = [8, 1, 1, 1, 2]

(for: 1. perturb constant, 2. mutate operator, 3. append a node, 4. delete a subtree, 5. do nothing).

TODO

Record hall of fame
Optionally migrate the hall of fame, rather than current bests