Published
Edited
Apr 19, 2021
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function simulate() {
const initial = Array.from({ length: N }, () => new Agent())
const gen = [initial];
for (let i = 0; i < r-1; ++i) {
let pop = gen[gen.length - 1]
let offspring = [];
pop.forEach((parent1) => {
// search for possible mate
let mates = pop.filter((candidate) => candidate !== parent1
&& parent1.inRadius(candidate)
)//&& parent1.compatible(candidate))
// if no nearby mates, do nothing
if (!mates.length) {
return;
}
mates.sort((a, b) => a.fitness - b.fitness)
let parent2 = mates.pop()
offspring.push(...parent1.createChildren(parent2));
})
let capacity = N;
let niches = [[], []]
offspring.forEach((o) => {
if (o.x > scaleX / 2) {
niches[1].push(o);
} else {
niches[0].push(o);
}
})
niches = niches.map(niche => niche.sort((a, b) => b.fitness - a.fitness)
.slice(0, N))
offspring = [...niches[0], ...niches[1]]
//offspring = offspring
// .sort((a, b) => b.fitness - a.fitness)
//.slice(0, N);
gen.push(offspring)
}
return gen
}
class Agent {
constructor(parent1, parent2) {
if (parent1 && parent2) {
this.initFromParents(parent1, parent2);
} else {
this.initWithoutParents();
}
}
initWithoutParents() {
this.x = randomUniform(0, scaleX / 2)();
this.gene = new Gene();
}
initFromParents(parent1, parent2) {
this.x = parent1.x + randomNormal(0, speed)()
if (this.x < 0) {
this.x = -this.x
} else if (this.x >= scaleX) {
this.x -= this.x - scaleX
}
this.gene = parent1.gene.recombinate(parent2.gene);
}
get fitness() {
const currentElevation = elevation(this.x)
let fractionOnes = this.gene.fractionOnes;
let fractionZeros = 1 - fractionOnes;
return currentElevation > 0 ? 1.0 + fractionOnes : 0.7 + fractionZeros;
}
inRadius(other) {
return Math.abs(this.x - other.x) < maxRadius;
}
compatible(other) {
return this.gene.compatible(other.gene)
}
createChildren(parent2) {
// Fitness is the expected value on the number of offspring
// If fitness is more than zero, create a second child with probability fitness - 1
// If fitness is less than zero, create a first child with probability fitness
const children = [];
let prob = this.fitness;
while (prob > 0) {
if (random() < prob) {
children.push(new Agent(this, parent2));
}
prob -= 1;
}
return children;
}
render() {
let fill = d3.interpolateSpectral(this.gene.fractionOnes)
let stroke = d3.color(fill).darker()
return `<g><circle cx=${this.x} cy=${y(this.x)} r=20 fill="${fill}" stroke="${stroke}"/><text x=${this.x} y=${y(this.x)}>${this.gene.numOnes}</text></g>`
}
}
//elevationMask = BitSet("11111")
class Gene {
constructor(bits) {
if (bits) {
this.bits = new BitSet(bits);
} else {
this.bits = new BitSet(); // all zeros
}
}
recombinate(other) {
const window = 3;
const newBitChunks = []
for (let i = GENE_SIZE; i >= 0; i -= window) {
// Pick random gene with equal probability for each slice of window size
const bits = random() < 0.5 ? this.bits : other.bits;
const newBits = bits.slice(Math.max(0, i - window), i - 1);
newBitChunks.push(newBits.toString().padStart(3, '0'));
}
const newBits = new BitSet(newBitChunks.join(""));
const mutate = random() < mutationProbability;
if (mutate) {
let j = Math.floor(random() * GENE_SIZE);
newBits.flip(j);
}
return new Gene(newBits)
}
get numOnes() {
return this.bits.cardinality();
}
get fractionOnes() {
return this.numOnes / GENE_SIZE;
}
_similarity(other) {
return (GENE_SIZE - this.gene.xor(other.gene).cardinality()) / GENE_SIZE;
}
compatible(other) {
let distance = this._similarity(other)
return distance > 0.1 && distance < 0.95;
}
}
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
