Chris Woody Woodruff is a solution architect, .NET Foundation board member, author, podcaster, and speaker.
Author
Chris shares his expertise through articles, technical documentation, and an upcoming book on network programming with C# and .NET. His writing makes complex software concepts clear and actionable.
Solution Architect
Chris Woody Woodruff designs scalable, high-performance software solutions, specializing in API design, cloud architecture, and modern development frameworks. He ensures technology meets business and user needs efficiently.
Speaker
A frequent speaker at conferences and industry events, Chris engages audiences on API strategy, database architecture, and software development. His talks inspire and educate developers worldwide.
"Every solution to every problem is simple. It’s the distance between the two where the mystery lies.”
Chris Woody Woodruff
Educator & Advocate
Just Stuff From Woody
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 well-designed visualization can clearly show how random routes...
Genetic algorithms thrive on randomness and gradual improvement, but randomness alone often leads to slow convergence. While global search is essential to explore the full solution space, local improvements can dramatically accelerate progress. That...
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 cities and the distances between them, the objective is...
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 fitness function communicates what "better" means in the...
Now that you’ve built the complete set of genetic algorithm components, chromosomes, fitness functions, mutation, crossover, selection, and a configurable loop, it’s time to apply everything in a hands-on project. In today’s post, we’ll...
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 is the configuration of the evolution...
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 convergence by deliberately preserving top-performing individuals across...
In yesterday’s post, we explored the importance of mutation in genetic algorithms. Mutation helps maintain genetic diversity, prevent premature convergence, and enable the discovery of better solutions through small, random changes. Today, we shift...
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 leaps. In genetic algorithms, mutation serves the same purpose:...