Genetic Algorithm OverflowException: Number overflow:

Hi i'm starting on genetic algorithm and i'm getting and overflow error, i know i'm getting a negative array on line 34 of my script and i think it's because it;s not getting initialized, i'm trying to make an AI that runs through and obstacle course and learns it through generations please help I apologize for the amount of code

using System; using System.Collections.Generic;

 public class GeneticAlgorithm<T>
 {
     public List<DNA<T>> Population { get; private set; }
     public int Generation { get; private set; }
     public float BestFitness { get; private set; }
     public T[] BestGenes { get; private set; }
 
     public int Elitism;
     public float MutationRate;
 
     private List<DNA<T>> newPopulation;
     private Random random;
     private float fitnessSum;
     private int dnaSize;
     private Func<T> getRandomGene;
     private Func<int, float> fitnessFunction;
 
     public GeneticAlgorithm(int populationSize, int dnaSize, Random random, Func<T> getRandomGene, Func<int, float> fitnessFunction,
         int elitism, float mutationRate = 0.01f)
     {
         Generation = 1;
         Elitism = elitism;
         MutationRate = mutationRate;
         Population = new List<DNA<T>>(populationSize);
         newPopulation = new List<DNA<T>>(populationSize);
         this.random = random;
         this.dnaSize = dnaSize;
         this.getRandomGene = getRandomGene;
         this.fitnessFunction = fitnessFunction;
 
         BestGenes = new T[dnaSize];  /*This is the array that gives me trouble*/
 
         for (int i = 0; i < populationSize; i++)
         {
             Population.Add(new DNA<T>(dnaSize, random, getRandomGene, fitnessFunction, shouldInitGenes: true));
         }
     }
 
     public void NewGeneration(int numNewDNA = 0, bool crossoverNewDNA = false)
     {
         int finalCount = Population.Count + numNewDNA;
 
         if (finalCount <= 0) {
             return;
         }
 
         if (Population.Count > 0) {
             CalculateFitness();
             Population.Sort(CompareDNA);
         }
         newPopulation.Clear();
 
         for (int i = 0; i < Population.Count; i++)
         {
             if (i < Elitism && i < Population.Count)
             {
                 newPopulation.Add(Population[i]);
             }
             else if (i < Population.Count || crossoverNewDNA)
             {
                 DNA<T> parent1 = ChooseParent();
                 DNA<T> parent2 = ChooseParent();
 
                 DNA<T> child = parent1.Crossover(parent2);
 
                 child.Mutate(MutationRate);
 
                 newPopulation.Add(child);
             }
             else
             {
                 newPopulation.Add(new DNA<T>(dnaSize, random, getRandomGene, fitnessFunction, shouldInitGenes: true));
             }
         }
 
         List<DNA<T>> tmpList = Population;
         Population = newPopulation;
         newPopulation = tmpList;
 
         Generation++;
     }
 
     private int CompareDNA(DNA<T> a, DNA<T> b)
     {
         if (a.Fitness > b.Fitness) {
             return -1;
         } else if (a.Fitness < b.Fitness) {
             return 1;
         } else {
             return 0;
         }
     }
 
     private void CalculateFitness()
     {
         fitnessSum = 0;
         DNA<T> best = Population[0];
 
         for (int i = 0; i < Population.Count; i++)
         {
             fitnessSum += Population[i].CalculateFitness(i);
 
             if (Population[i].Fitness > best.Fitness)
             {
                 best = Population[i];
             }
         }
 
         BestFitness = best.Fitness;
         best.Genes.CopyTo(BestGenes, 0);
     }
 
     private DNA<T> ChooseParent()
     {
         double randomNumber = random.NextDouble() * fitnessSum;
 
         for (int i = 0; i < Population.Count; i++)
         {
             if (randomNumber < Population[i].Fitness)
             {
                 return Population[i];
             }
 
             randomNumber -= Population[i].Fitness;
         }
 
         return null;
     }
 }

 using System.Collections;
 using System.Collections.Generic;
 using UnityEngine;
 
 public class GAManager : MonoBehaviour {
 
     private GeneticAlgorithm<MovementData> ga;
     private System.Random random;
 
     //Movement
     Transform finish;
     Transform start;
     float xstart;
     float xfinish;
     int distance;
     int topScore = 15;
     int maxSpeed = 15;
     int minSpeed = 1;
     int maxJump = 15;
     int minJump = 1;
 
     //Genetic Algorithm
     public int populationSize = 1;
     int elitism = 1;
     float mutationRate = 0.01f;
 
     // Use this for initialization
     void Start () {
         finish = GameObject.Find("Finish").GetComponent<Transform>();
         start = GameObject.Find("Start").GetComponent<Transform>();
         xstart = start.position.x;
         xfinish = finish.position.y;
         distance = (int)xstart - (int)xfinish;
         random = new System.Random();
         ga = new GeneticAlgorithm<MovementData>(populationSize, distance, random, GetRandomMovement, FitnessFunction, elitism, mutationRate);
     }
     
     // Update is called once per frame
     void Update () {
 
     }
 
     public MovementData GetRandomMovement ()
     {
         MovementData movementData = new MovementData();
     movementData.speed = Random.Range (minSpeed, maxSpeed);
         movementData.jumpForce = Random.Range(minJump, maxJump);
         return movementData;
     }
 
     private float FitnessFunction(int index)
     {
         float score = 0;
         DNA<MovementData> dna = ga.Population [index];
 
         for (int i = 0; i < dna.Genes.Length; i++) 
         {
             if (dna.Genes [i].distanceAI == distance)
             {
                 score += 1;
             }
 
             score += dna.Genes[i].triggersHad / topScore;
         }
 
         return score;
     }
 
     void OnTriggerEnter(Collider other)
     {
         MovementData movetriggers = new MovementData ();
         if (other.tag == "AddScore") {
             Debug.Log("Increase Score");
             movetriggers.triggersHad++;
         } else if (other.tag == "DecreaseScore") {
             Debug.Log("Decrease Score");
             movetriggers.triggersHad--;
         }
     }
 
 }
 // This is my the manager that gets all the data i need for the AI


 using System;
 using System.Collections;
 using System.Collections.Generic;
 using UnityEngine;
 
 [Serializable]
 public class MovementData
 {
     public float speed;
     public float jumpForce;
     public float distanceAI;
     public int triggersHad;
 }
 
 public class Movement : MonoBehaviour {
 
     public Rigidbody rb;
     public float gravity;
     public bool hasJumped;
     public MovementData movementData;
 
     private GeneticAlgorithm<MovementData> ga;
 
     public enum State
     {
         Jump,
         Run
     }
 
     public State state;
 
     // Use this for initialization
     void Start () {
         rb = GetComponent<Rigidbody>();
 
         state = State.Run;
     }
     
     // Update is called once per frame
     void Update () {
 
         if (Input.GetButtonDown("Jump"))
         {
             state = State.Jump;
         }
         else if(Input.GetButtonUp("Jump"))
         {
                 state = State.Run;
         }
 
         switch (state)
         {
             case State.Jump:
                 {
                     Jump();
                     break;
                 }
             case State.Run:
                 {
                     Run();
                     break;
                 }
         } 
     }
 
     void Jump()
     {
         rb.AddForce(Vector3.up * movementData.jumpForce, ForceMode.Impulse);
         rb.AddForce(Physics.gravity * gravity, ForceMode.Acceleration);
         hasJumped = true;
     }
 
     void Run()
     {
         rb.AddForce(Vector3.right * movementData.speed, ForceMode.Force);
         hasJumped = false;
     }
 }
 
 
 // And this is a simple AI movement script i made maybe it's relevant thank you for your help