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 the week of June 2, I’m launching a 37-day blog series—a 5-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.
Here’s Your Day-by-Day Bootcamp Lineup:
- The Survival of the Fittest Code: Why Learn Genetic Algorithms in C#?
- What Are Genetic Algorithms? A Developer’s Guide to Evolutionary Logic
- Understanding Chromosomes, Genes, and DNA in Code
- Designing Your First Chromosome Class in C#
- Natural Selection in Software: Implementing Fitness Functions
- Roulette, Tournaments, and Elites: Exploring Selection Strategies
- Putting It Together: Simulating Your First GA Cycle in .NET
- One Point or Two? How Crossover Shapes Genetic Diversity
- Uniform Crossover in C#: Combining Chromosomes with Balance
- Mutation Matters: How Small Changes Spark Big Improvements
- Implementing a Mutation Operator with Randomness in Mind
- Elitism in Evolution: Preserving the Best Code
- Configuring the GA Loop: Population, Generations, and Mutation Rates
- Evolving Text: Solving the “Hello World” Puzzle with a GA
- Fitness by Design: How to Shape the Problem to Match Evolution
- Solving the Traveling Salesperson Problem with Permutation Chromosomes
- Greedy Isn’t Always Bad: Heuristics in Genetic Algorithms
- Mapping Cities: Visualizing TSP Evolution in .NET
- Scheduling with DNA: Using GAs for Class and Work Timetables
- Constraint Handling in Fitness Functions: Penalizing Bad Solutions
- Genetic Algorithms vs. Brute Force: A Benchmark Comparison
- Multi-Objective Optimization: When One Fitness Function Isn’t Enough
- Introduction to NSGA-II in C#
- Combining GAs with Hill Climbing: The Hybrid Memetic Approach
- Scaling Up: Parallelizing GA Loops in .NET with Parallel.ForEach
- Running GAs in the Cloud with Azure Batch or Functions
- Logging and Monitoring Genetic Progress Over Generations
- Visualizing Evolution: Building a Real-Time Chart of Fitness
- Building a Pluggable GA Framework in C#
- Defining Interfaces for GA Components: Fitness, Selection, and Operators
- Unit Testing Your Evolution: Making GAs Testable and Predictable
- Best Practices for Tuning Genetic Algorithm Parameters
- When GAs Go Wrong: Debugging Poor Performance and Premature Convergence
- Case Study: Using a GA to Optimize Hyperparameters in a Neural Network
- GA vs. Other Optimization Techniques: A Developer’s Perspective
- Evolution Beyond Biology: Using GAs for Creative Art and Design
- Final Reflections: 37 Days of Evolutionary Coding
What You’ll Learn Along the Way
- How to model evolution in code with fitness functions, crossover, and mutation
- How to build a fully functional GA engine in C# and .NET
- How to apply GAs to real-world challenges like TSP, scheduling, and hyperparameter tuning
- How to refactor your experiments into reusable, testable frameworks
- How to visualize and debug AI-like behavior in your applications
Join the Experiment
Follow the series here on the blog or subscribe for updates via RSS or GitHub. You’ll get daily doses of insight, complete code walkthroughs, and challenges to try independently. Whether you’re a .NET pro or just curious about AI, this is your chance to build something truly evolutionary.
Let’s evolve some code.