Day 9: Using Genetic Algorithm’s Uniform Crossover in C#

So far, we’ve explored one-point and two-point crossover strategies, which split chromosomes at predefined positions. These methods are effective for maintaining gene sequence structure, but they can be limiting when diversity is crucial. Enter uniform crossover—a technique that treats each gene position independently, offering greater mixing and a finer-grained approach to recombination.

Today, we’ll implement uniform crossover in C#, compare it with other strategies, and explore when and why you should use it.

What Is Uniform Crossover?

Uniform crossover is a recombination method that operates on a per-gene basis. Instead of copying a block of genes from one parent and then the rest from another, the algorithm flips a coin (or generates a random number) for each gene to decide which parent contributes that gene to the child.

This strategy maximizes variation and balances inheritance across the entire chromosome.

Benefits of Uniform Crossover

• High Diversity: Each gene is a 50/50 decision, promoting exploration.
• Fine-Grained Control: No dependence on contiguous gene segments.
• Stable Mixing: Reduces bias in gene inheritance across generations.

It is especially effective when genes are independent or when the order of genes is not critical (e.g., in binary encodings or feature selection problems).

Implementing Uniform Crossover in C#

Here is a simple and efficient implementation in C#:

public Chromosome UniformCrossover(Chromosome partner)
{
    int length = Genes.Length;
    char[] childGenes = new char[length];

    for (int i = 0; i < length; i++)
    {
        bool takeFromThis = Random.Shared.NextDouble() < 0.5;
        childGenes[i] = takeFromThis ? Genes[i] : partner.Genes[i];
    }

    return new Chromosome(childGenes);
}

This method uses a random value at each index to decide which parent’s gene to use. Over many offspring, this produces a well-mixed gene pool.

Parameterized Uniformity

You can introduce a crossover probability to control how often genes are exchanged. For example, a 0.7 uniformity rate would take genes from the first parent 70 percent of the time.

public Chromosome UniformCrossover(Chromosome partner, double uniformityRate)
{
    int length = Genes.Length;
    char[] childGenes = new char[length];

    for (int i = 0; i < length; i++)
    {
        bool takeFromThis = Random.Shared.NextDouble() < uniformityRate;
        childGenes[i] = takeFromThis ? Genes[i] : partner.Genes[i];
    }

    return new Chromosome(childGenes);
}

This gives you the flexibility to adjust the balance between exploitation (using the stronger parent more often) and exploration (mixing aggressively).

Example Comparison

Let’s say you have:

• Parent A: HELLO WORLD
• Parent B: YXLQR PZKMG

One-point crossover at index 5 might yield:

• Child: HELLO PZKMG

Uniform crossover might result in:

• Child: HXLLO PRKLD

Notice how uniform crossover mixes genes from all parts of both parents, not just contiguous sections. This increases the genetic variety across generations, which is crucial for escaping local optima.

When to Use Uniform Crossover

Uniform crossover is especially useful when:

• Genes are loosely ordered or unordered
• You want to avoid gene dominance
• Maintaining specific sequences is not critical

However, it can disrupt valuable gene combinations (called building blocks) if the gene sequence has structural importance, such as in routing or scheduling problems. In those cases, one- or two-point crossover may be safer.

Up Next

Now that you’ve seen how crossover affects diversity, tomorrow we’ll focus on mutation—the small but powerful tweak that helps escape evolutionary dead ends and ensures your population keeps improving.

Recombination brings the big moves, but mutation provides the spark.