index.html

    function startNewGen() {
      totalFitness = endGen();
      let newCarArr = []; // The next generation

      mutationRate = mrateInput.value() // Pull mutation rate

      //Get parent weights
      let parentWeights = [];
      cars.forEach((c) => {
        parentWeights.push(c.copyWeights());
      });

      //Create next generation
      for (let i = 0; i < 10; i++) {
        // Initialize Child
        const child = new Car(
          outerDi,
          innerDi,
          screenHt,
          screenWd,
          carDi,
          obsts
        );

        let currWeights = child.model.getWeights();
        let newWeights = [];

        for (let i = 0; i < currWeights.length; i++) {
          // Iterate through each layer
          //Get Current Weight information
          let currVals = currWeights[i].dataSync(); // Array format
          let shape = currWeights[i].shape;

          for (let j = 0; j < currVals.length; j++) {
            // Iterate through each weight
            // Select trait from random parent
            currVals[j] = parentWeights[selectRandParent(totalFitness)][i][j];
            // Assign random value to weight from Gaussian Distribution(Function from P5)
            if(random() < mutationRate) {
                currVals[j] += randomGaussian()
            }
          }
          let newTens = tf.tensor(currVals, shape);
          newWeights[i] = newTens;
        }

        child.model.setWeights(newWeights);

        newCarArr.push(child);
      }

      cars = newCarArr;
      // Increment Generation and Update Label
      currGen += 1;
      infoLbl.elt.innerText =
        "Gen: " + currGen + ", Top Score: " + topFitnessScore;
    }