## Genetic algorithm, Hello World

### 2018

Step 1 Create a population. Here 30 particles whith a constant accélération Step 2 Give some “DNA”. In this example DNA is an array of 200 random directions (normalized random 2D vector) that will be read sequentially each frame Step 3 Evaluate/crossover/mutate the population.
My fitness function evaluate the distance of the ‘robot’ from the target, if he ‘crash’ too far, his dna should be eliminated (fitness = 0) but i always keep 10% of the previous generation wathever the fitness, in order to keep some diversity. ```this.calcFitness = function()
{
var dx = obstacles.x - this.pos.x; // obstacles is the target
var dy = obstacles.y - this.pos.y;
var d = Math.sqrt(dx*dx + dy*dy);

this.fitness = Math.min(1,TARGET_SIZE / d);

if(this.isBroken) // has crashed (screen border or obstacle)
{
if(d > WIDTH2) // too far !
{
this.fitness = 0;
}
}
}
```
```this.crossover = function(other)
{
var result = [];
var limit = Math.floor(Math.random() * this.genes.length);
for(var i = 0 ; i < this.genes.length ; i++)
{
if(i <limit)
{
result[i] = this.genes[i];
}else{
result[i] = other.genes[i];
}
}

return new DNA(result);
}
```
```this.mutation = function()
{
const POW = 1;
for(var i = 0 ; i < this.genes.length ; i++)
{
if(Math.random() < GENE_MUTATION_CHANCE)
{
this.genes[i] = new Vector2D(POW * getRandomFloat(-1,1), POW * getRandomFloat(-1,1));
}
}
}
```

Step 4 Add some graphics and obstacles and have fun :)

Step 5 [TODO] Add some interactivity (sliders), include time in the calcFitness function