Published
Edited
Mar 27, 2020
defineAlgorithm = {
var geneticAlgorithm = new geneticalgorithm({
mutationFunction: mutationFunction,
crossoverFunction: crossoverFunction,
fitnessFunction: fitnessFunction,
population: [ { numbers : [ 0 , 0 , 0 ] } ]
});
clearResults();
for( var i = 0 ; i < 40 * TARGET_LENGTH ; i++ ) {
geneticAlgorithm.evolve();
results.push(geneticAlgorithm.best());
};
}
function mutationFunction(phenotype) {
phenotype = cloneJSON(phenotype)
var chance = Math.random()
var index = Math.floor( Math.random() * phenotype.numbers.length )
// remove one number
if ( chance < 0.1 && phenotype.numbers.length > 0 ) {
phenotype.numbers.splice( index , 1 )
return phenotype
// add one number
} else if ( chance < 0.2 ) {
var numbers = phenotype.numbers.slice( 0 , index )
.concat( phenotype.numbers.slice( index , index+1 ) )
.concat( phenotype.numbers.slice( index ) )
phenotype.numbers = numbers
return phenotype
// mutate
} else {
phenotype.numbers[index] += Math.random() * 20 - 10;
return phenotype;
}
}
function crossoverFunction(a, b) {
if ( a.numbers.length > b.numbers.length ) {
var temp = a
a = b
b = temp
}
var x = cloneJSON(a), y = cloneJSON(b), cross = false, offset = 0;
for (var i in a.numbers) {
if ( a.numbers.length + offset < b.numbers.length ) {
offset += Math.random() * a.numbers.length <= 1 ? 1 : 0
}
if ( Math.random() * a.numbers.length <= 1 ) { cross = !cross }
if (cross) {
x.numbers[i] = b.numbers[i];
y.numbers[i+offset] = a.numbers[i];
}
}
return [ x , y ];
}
function fitnessFunction(phenotype) {
if ( phenotype.numbers.length == 0 ) { return 0 }
var sumOfPowers = 0;
for (var i in phenotype.numbers) {
// assume perfect solution is '50.0' for all numbers
sumOfPowers += Math.pow( 50 - phenotype.numbers[i], 2);
}
var result = 1 / Math.sqrt(sumOfPowers)
result /= Math.max( Math.pow(0.01,TARGET_LENGTH) , Math.abs( phenotype.numbers.length - TARGET_LENGTH ) )
return result
}
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