index.html

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

        //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];
            }
            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;
      }