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Genetic algorithms (GA) are inspired by the process of natural selection, and are commonly used to generate high-quality solutions to optimization problems by relying on biologically inspired operators such as crossover, mutation and selection.

The problem of finding the “good enough” combination of genes for the car looks like an optimization problem, so there is a good chance that GA will help us here.

Step-by-step we’re going to break down a high-level task of creating the self-parking car to the straightforward low-level optimization problem of finding the optimal combination of 180 bits (finding the optimal car genome).

You may launch the 🚕 Self-parking Car Evolution Simulator to see the evolution process directly in your browser. The simulator gives you the following opportunities:

• You may train the cars from scratch and adjust genetic parameters by yourself
• You may see the trained self-parking cars in action
• You may also try to park the car manually

The genetic algorithm for this project is implemented in TypeScript. The full genetic source code will be shown in this article, but you may also find the final code examples in the Evolution Simulator repository.