When it comes to understanding algorithms, there’s an old adage that says, “seeing is believing.” The intricate dance of logic and calculation that an algorithm performs is often better understood visually. This is especially true for Genetic Algorithms (GAs), a nature-inspired approach to problem-solving. With that in mind, I created a web app with Svelte to demonstrate a basic GA.

A Quick Primer on Genetic Algorithms

At their core, GAs are inspired by the process of natural selection. They operate on a population of potential solutions, evolving this population over time. Through mechanisms like selection, crossover (reproduction), and mutation, a GA gradually refines its population, aiming to converge on an optimal solution.

In our project, I attempted a simple task: evolve a random population of strings to match a target string. For instance, if the target is “HELLO,” the GA starts with random strings and iterates over generations, tweaking and refining, until one of them eventually spells “HELLO.”

Why Svelte?

Svelte is a component-based JavaScript framework that compiles components into highly efficient vanilla JavaScript at build time. This means our visualizations are lightning-fast, ensuring we can keep up with the rapid calculations of our GA and offer a smooth, real-time experience.

The Visualization’s Functional Aspects

The developed application presents users with a clear interface featuring controls to initiate, pause, or restart the GA’s process. As the algorithm runs, the evolving population of strings is displayed, giving users an immediate view of each generation’s state.

To provide an understanding of the population’s proximity to the target, a progress bar represents how closely the best-performing string of the current population matches the target.

Wrapping Up

The marriage of Genetic Algorithms and Svelte in this project provided a functional tool to understand the workings of GAs in a more tangible manner. By visualizing the evolution process step by step, we are offered a clearer perspective on the algorithm’s operational nuances. For those interested in a hands-on exploration, the project is available on GitHub for further examination.

Link to Repo

Genetic Algorithm