Bringing Simplicity-First to the Page: My Upcoming Book
I am excited to share some big news. In the second half of 2026, my new book, titled Software Architecture Made Simple: A 'Simplicity-First' Approach to Software in the…
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AI
38 articles.
Day 35: Evolution Beyond Biology: Using Genetic Algorithms for Creative Art and Design
Genetic Algorithms are often associated with engineering, scheduling, or optimization problems, but their potential extends into the domain of art and design. When…
Day 34: Genetic Algorithms vs. Other Optimization Techniques: A Developer's Perspective
Genetic Algorithms (GAs) are a powerful optimization strategy inspired by the principles of natural evolution. But they are far from the only technique in a developer's…
Day 33: Case Study: Using a Genetic Algorithms to Optimize Hyperparameters in a Neural Network
Tuning hyperparameters for machine learning models like neural networks can be tedious and time-consuming. Traditional grid search or random search lacks efficiency in…
Day 32: When Genetic Algorithms Go Wrong: Debugging Poor Performance and Premature Convergence
Even well-written Genetic Algorithms can fail. You might see little improvement over generations, results clustering around poor solutions, or a complete stall in…
Day 31: Best Practices for Tuning Genetic Algorithm Parameters
Genetic Algorithms (GAs) are flexible and powerful tools for solving optimization problems. However, their effectiveness relies heavily on the correct tuning of…
Day 30: Unit Testing Your Evolution: Making Genetic Algorithms Testable and Predictable
Genetic Algorithms are inherently stochastic. Mutation introduces randomness. Crossover combines genes in unpredictable ways. Selection strategies often rely on…
Day 29: Defining Interfaces for Genetic Algorithms Components: Fitness, Selection, and Operators
To build flexible and maintainable genetic algorithm solutions in C, a modular architecture is critical. Yesterday, we focused on designing a pluggable GA framework.…
Day 28: Building a Pluggable Genetic Algorithms Framework in C#
As you reach the final week of our Genetic Algorithms series, it is time to shift from experimentation to engineering. Instead of writing one-off implementations…
Day 27: Logging and Monitoring Genetic Algorthms Progress Over Generations
As your genetic algorithms become more sophisticated, it's no longer enough to simply observe the final output. Monitoring the evolutionary process in real time provides…
Day 26: Running Genetic Algorthms in the Cloud with Azure Batch or Functions
As your genetic algorithm workloads grow in complexity, compute-intensive tasks like evaluating large populations or running many generations can exceed what a single…
Day 25: Scaling Up: Parallelizing Genetic Algorithms Loops in .NET with Parallel.ForEach
As problem complexity grows, so does the cost of evaluating and evolving populations in genetic algorithms. When each individual's fitness computation becomes expensive…
Day 24: Combining Genetic Algorithms with Hill Climbing: The Hybrid Memetic Approach
Traditional genetic algorithms (GAs) excel at global exploration across large search spaces. However, they can struggle to fine-tune solutions with high precision due to…
Day 23: Introduction to Non-dominated Sorting Genetic Algorithm II (NSGA-II) in C#
As we extend our use of genetic algorithms (GAs) beyond single-objective problems, we enter the realm of multi-objective optimization, where trade-offs must be made…
Day 22: Multi-Objective Optimization: When One Fitness Function Isn't Enough
In many real-world problems, a single fitness function is insufficient to capture the complexity of the solution space. Applications in engineering, logistics, finance…
Day 21: Genetic Algorithms vs. Brute Force: A Benchmark Comparison
To conclude Week 3, let’s address one of the most common questions developers ask when learning about genetic algorithms: How do they perform compared to brute-force…
Day 20: Constraint Handling in Fitness Functions: Penalizing Bad Solutions
Genetic algorithms are powerful optimization tools, but real-world problems often involve constraints that cannot be ignored. In scheduling, routing, resource…
Day 19: Scheduling with DNA: Using Genetic Algorthms for Class and Work Timetables
Scheduling is a classic example of a constraint satisfaction problem that often becomes too complex for brute-force or greedy solutions. Whether you're designing class…
Day 18: Mapping Cities: Visualizing TSP Evolution in .NET
One of the most effective ways to understand the progress of a genetic algorithm is to visualize its evolution. When solving the Traveling Salesperson Problem, a…
Day 17: Greedy Isn't Always Bad: Heuristics in Genetic Algorithms
Genetic algorithms thrive on randomness and gradual improvement, but randomness alone often leads to slow convergence. While global search is essential to explore the…
Day 16: Solving the Traveling Salesperson Problem with Genetic Algorithms Permutation Chromosomes
The Traveling Salesperson Problem, also known as TSP, is one of the most extensively studied combinatorial optimization problems in computer science. Given a set of…
Day 15: Fitness by Design: How to Shape the Problem to Match Evolution
In genetic algorithms, the fitness function is not just a scoring system. It is the definition of success. Your entire evolutionary process hinges on how well the…
Day 14: Evolving Text: Solving the "Hello World" Puzzle with a C# Genetic Algorithm
Now that you’ve built the complete set of genetic algorithm components, chromosomes, fitness functions, mutation, crossover, selection, and a configurable loop, it’s…
Day 13: Configuring the Genetic Algorithm Loop in C#
A genetic algorithm is only as effective as the loop that drives it. While selection, crossover, mutation, and elitism form the backbone of a genetic algorithm (GA), it…
Day 12: Genetic Algorithms' Elitism for Evolution Survival of the Fittest
Natural selection favors the survival of the fittest, but evolution in the wild is not always efficient. In genetic algorithms, we can bias the process toward faster…
Day 11: Implementing a C# Mutation Operator for Genetic Algorithms
In yesterday’s post, we explored the importance of mutation in genetic algorithms. Mutation helps maintain genetic diversity, prevent premature convergence, and enable…
Day 10: Mutation Matters in C# Genetic Algorithms
In biological evolution, mutations are rare, random changes in DNA that introduce new traits. While many mutations are neutral or even harmful, some spark evolutionary…
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…
Day 8: One Point or Two? How Crossover Shapes Genetic Diversity
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…
Day 7: Putting It Together: Simulating Your First Genetic Algorthm Cycle in .NET
By now, you’ve learned the foundational components of genetic algorithms: chromosomes, genes, fitness functions, mutation, crossover, and selection. Today, it’s time to…
Day 6: Roulette, Tournaments, and Elites: Exploring Selection Strategies
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…
Day 5: Natural Selection in Software: Implementing Fitness Functions
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…
Day 4: Designing Your First Chromosome Class in C#
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…
Day 3: Understanding Chromosomes, Genes, and DNA in Code
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…
Day 2: Evolution in Code: The Core Concepts
At their core, genetic algorithms are built on five foundational principles that closely resemble biological evolution: In biology, genes are units of information, and…
Mechanical Minds and Human Folly: Why Fearin' AI is More Foolish Than Fruitful
It has come to my attention recently that many good folks have grown anxious over this new contrivance known as Artificial Intelligence. They eye these ingenious…
Day 1: The Survival of the Fittest Code: Why Learn Genetic Algorithms in C#?
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…
Evolve Your C# Code with AI: A 5-Week Genetic Algorithms Bootcamp for Developers
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…