A mix of different genetic inspired algorithms and experiments in Nim.

To run these you need to install Nim

Differential evolution

You got a problem and it sounds like an optimization problem, you don't want to juggle many equations with many unknowns in a real math sense. You want to know if there is a solution.

There are 36 heads and 100 legs, how many horses and jockeys are there?

If you can express this in code you can run this algorithm to find an answer. You express this by a function that takes an array of parameters you want to find (two in this case), and you implement the function so that it returns a small number if the solution is good, and a big number if the solution is bad.

proc horses_and_jockeys*(x: openarray[float]): float =
  let
    horses = x[0]
    jockeys = x[1]
    legs = horses*4 + jockeys*2
    heads = horses + jockeys
  result = abs(36-heads) + abs(100-legs)

Set the hyperparams to something sensible, i.e. 2 params, positive bounds

const
  log_csv = false
  print = 1000
  optimizer = horses_and_jockeys
  params = 2
  bounds = 0.0..1000.0
  generations = 10000
  popsize = 1000
  mutate_range = 0.2..0.95
  crossover_range = 0.1..1.0

[14.0, 22.0]

It also works if you have constraints.

In League of Legends, a player's Effective Health when defending against physical damage is given by E=H(100+A)/100, where H is health and A is armor. Health costs 2.5 gold per unit, and Armor costs 18 gold per unit. You have 3600 gold, and you need to optimize the effectiveness E of your health and armor to survive as long as possible against the enemy team's attacks. How much of each should you buy?

proc lol1*(x: openarray[float]): float =
  let
    health = x[0]
    armor = x[1]
    effective_hp = (health*(100.0+armor))/100.0
  if (health*2.5 + armor*18) > 3600:
    return 1.0
  return 1.0/effective_hp

const
  log_csv = false
  print = 1000
  optimizer = lol1
  params = 2
  bounds = 0.0..10000.0
  generations = 10000
  popsize = 1000

[1079.999993591275, 50.00000089010068] You do not spend equal money on A and H: E=3H−1720H2 so the maximum is at H=1080, plug back in for A=50.

The algorithm by no means guarantees that this is the best solution.

Try it:

1. Clone the repo
2. Install Nim
3. Go to "DifferentialEvolution" folder
4. Type in your terminal nim c -r -d=danger -l=-flto --passC:-ffast-math app.nim

Evolving neural nets

Genetic programming

Genetic algorithm