Abstract
Many real-world problems like scheduling, routing, and resource allocation are too complex for deterministic or greedy algorithms to solve effectively. When the solution space is chaotic, non-differentiable, or constrained by real-world uncertainty, we need smarter search strategies. That’s where Evolutionary Algorithms (EAs) come in.
This talk demonstrates how Genetic Algorithms (GAs), a powerful subset of EAs, can be paired with Rust’s core strengths—performance, ownership, and traits—to build fast, modular, and parallelizable solutions. I’ll show how Rust’s type system and concurrency model make it uniquely suited to implementing evolutionary approaches that are robust, scalable, and often more flexible than traditional methods.
More specifically I'll work through the popular traveling salesman problem during my presentation to showcase a practical application of Rust in the domain of Evolutionary Algorithms. The audience will hopefully get a sense of a new way of using Rust and it's features to solve NP-hard style problems.
The talk will include a live demo to visualize how all the components come together to solve the problem.
