Category: Genetic Algorithms

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.