Tag: programming

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.

In the evolutionary process, crossover is the mechanism by which parents pass on their traits to offspring. In genetic algorithms, crossover plays the same role, combining genes from two parent chromosomes to produce new solutions. How you implement crossover significantly impacts the algorithm’s ability to explore the search space and avoid premature convergence.

Today, we dive into crossover methods in C#, comparing one-point, two-point, and uniform crossover, and how each influences genetic diversity.

By now, you’ve learned the foundational components of genetic algorithms: chromosomes, genes, fitness functions, mutation, crossover, and selection. Today, it’s time to bring those elements together and run your first complete GA cycle using C# and .NET.

This post walks you through the structure of a single evolutionary loop—initialization, evaluation, selection, reproduction, and mutation—so you can simulate a working genetic algorithm and evolve real solutions.

Once you’ve calculated the fitness of each chromosome in your population, the next step in the genetic algorithm lifecycle is selection—deciding which chromosomes get to reproduce and which are left behind.

Selection strategies play a crucial role in balancing exploration (searching new areas of the solution space) and exploitation (refining known good solutions). Choosing the right strategy affects the convergence speed and the overall effectiveness of your genetic algorithm.

Today, we’ll explore the most common selection strategies: roulette wheel selection, tournament selection, and elitism. We’ll compare them and implement each one in C#.

In the natural world, organisms survive and reproduce based on their ability to adapt to their environment. This principle of natural selection is central to the effectiveness of genetic algorithms. In software, our analog to survival is fitness—a quantitative measurement of how well a solution performs.

Today, we focus on the role of fitness functions in guiding evolutionary progress in a genetic algorithm, and we’ll implement them in C# to evaluate and score candidate solutions effectively.

Now that we’ve explored the concept of genes and chromosomes in the context of genetic algorithms, it’s time to write some real code. Today’s goal is to design a reusable, extensible Chromosome class in C# that can serve as the foundation for solving optimization problems using genetic algorithms.

We will not only model the chromosome itself, but also lay the groundwork for operations such as initialization, crossover, mutation, and evaluation. Think of this class as the central actor in your evolutionary simulation.

At the heart of every genetic algorithm lies the concept of evolution, and at the heart of evolution lies DNA. For software developers, the equivalent building blocks are chromosomes and genes. If we want our applications to evolve solutions over time, we need a reliable way to encode, manipulate, and assess those building blocks in our C# programs.

Today, we’ll take a closer look at how we can represent chromosomes and genes in C#, how to choose the right data structures, and how to build a model that is both flexible and performant.

At their core, genetic algorithms are built on five foundational principles that closely resemble biological evolution: 1. Genes and Chromosomes […]

What if you could write code that evolves? Not just code that runs, but code that iteratively improves its own solutions to complex problems without requiring you to handcraft every edge case. That’s the promise of genetic algorithms (GAs), an AI-inspired method rooted in Darwinian evolution, and it fits surprisingly well in the world of modern C# development.

What if your code could evolve like life itself—adapting, optimizing, and learning over time? Welcome to the AI-inspired world of Genetic Algorithms, where we blend evolution with code to solve complex problems cleverly.

Starting this week, I’m launching a 42-day blog series—a 4-week bootcamp—designed to teach C# and .NET developers how to build, run, and scale Genetic Algorithms. From foundational concepts to solving real-world optimization problems, this series is your guide to coding like Darwin meant it.

Using clean, testable C# code, we’ll simulate survival of the fittest with fitness functions, crossover operations, mutations, and elite selection. This isn’t theoretical fluff—it’s practical, hands-on AI for your everyday dev life. Whether you’re optimizing routes, building smarter schedules, or just curious how to make your software think, this series is for you.