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
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:...
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...
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...
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...
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...